Pith. sign in

REVIEW 4 major objections 6 minor 1 cited by

A survey on cutting-edge relation extraction techniques based on language models

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read BERT-based models dominate state-of-the-art relation extraction in the recent ACL-conference literature, while large language models like T5 show promise mainly in few-shot scenarios.

desk verdict Useful survey of 2020-2023 ACL-family RE work, but the abstract's few-shot T5 claim is contradicted by the paper's own Table 7 and should be cut or heavily qualified before publication. read the letter →

arxiv 2411.18157 v1 pith:BQ6K5K3D submitted 2024-11-27 cs.CL cs.AI

Jose A. Diaz-Garcia , Julio Amador Diaz Lopez This is my paper · ORCID
classification cs.CLcs.AI
keywords relationextractionlanguagemodelsBERTRoBERTalargefew-shotTACREDDocRED
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey tries to establish where relation extraction (RE) actually stands after four years of language-model-driven research. Reviewing 137 papers from the ACL family of conferences (2020–2023), with 65 meeting its inclusion criteria as research contributions, it argues that BERT-based encoders are the dominant recipe: BERT appears in nearly 55% of the papers, and BERT with RoBERTa supplies 75% of the top results in the three headline benchmark tables it compares (TACRED, DocRED, FewRel). The paper also claims that large language models such as T5 and GPT are currently peripheral in most RE settings, but lead in few-shot and zero-shot scenarios, where they identify previously unseen relations. A sympathetic reader would care because the survey maps a scattered literature into a picture of which models, tasks, and datasets are actually driving progress.

What carries the argument

The machine that carries the argument is the survey's curated corpus and its three comparative benchmark tables. The corpus is assembled by searching ACL, NAACL, AACL, and EACL proceedings for “Relation Extraction” in title or abstract and then applying inclusion and exclusion criteria (dropping temporal RE, NER-focused work, non-LM methods, and non-adopted encodings), yielding 65 research papers; the benchmark tables then rank the top five systems on TACRED, DocRED, and FewRel. These tables are what make the 75% BERT/RoBERTa claim concrete, and the paper's model-usage table links that performance dominance to adoption rates across papers.

What would settle it

A concrete check would be to rebuild the three top-five tables (TACRED, DocRED, FewRel) with systems published through 2024 at all major NLP and ML venues, not just the four ACL-family conferences, and count what fraction of top results are BERT/RoBERTa versus LLM-based; if LLMs take a majority of the top entries, the 75% split is an artifact of corpus scope rather than a property of the field.

Watch

Extended reading notes

Core claim

The central claim, stated in the abstract and supported in Section 6, is that BERT-based methods remain the state of the art for relation extraction. In the survey's comparison of the top five systems on TACRED (sentence-level), DocRED (document-level), and FewRel (few-shot), BERT and RoBERTa models hold 75% of the primary outcomes; RoBERTa-large is the encoder behind every top DocRED system, which the authors explain through RoBERTa's larger pretraining corpus, exclusive masked-language-model objective, and longer sequence handling. Large language models such as T5 and GPT contribute roughly 25% of the top outcomes and are especially effective in few-shot and zero-shot settings, where they can handle relations unseen in training. The paper also claims these LLMs are underused in the field, appearing in only about 8.5% of the papers, and attributes this to BERT's architectural alignment with RE rather than to model accessibility.

Load-bearing premise

The whole BERT-dominance conclusion rests on the assumption that the 65 papers selected from ACL, NAACL, AACL, and EACL between 2020 and 2023, together with the authors' inclusion and exclusion criteria, fairly represent the broader field of relation-extraction research.

Editorial extensions

If this is right

  • Practitioners seeking top accuracy on standard RE benchmarks can still start from a fine-tuned BERT or RoBERTa encoder rather than a large generative model.
  • TACRED, DocRED, and FewRel function as the field's de facto evaluation triad, so new RE systems should report on all three to be directly comparable.
  • For document-level RE, the winning recipe is RoBERTa-large-style pretraining with long sequences and no next-sentence prediction, pointing future work toward context-window extensions.
  • Large language models should be targeted at few-shot and zero-shot settings, where their demonstrated advantage is generalizing to unseen relations.
  • The overall trajectory is toward transformer-based methods, with CNN, LSTM, and static word-embedding baselines fading out of the surveyed literature after 2021.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the same benchmark comparison were widened to EMNLP, ICLR, and NeurIPS papers from 2023–2025, the 75/25 BERT-to-LLM split could narrow, since the survey window ends before large instruction-tuned models were routinely evaluated on RE; the dominance claim should be read as true for the ACL-family corpus it sampled.
  • The paper's final remark on relation “domain size” suggests a testable design rule: match model memory or generalization capacity to the cardinality of the relation type, which could be evaluated with per-relation F1 breakdowns on TACRED and FewRel.
  • The low 8.5% adoption of LLMs may be a lagging indicator rather than a ceiling; if the few-shot advantage on unseen relations compounds with longer context windows, document-level RE is the most likely place for LLMs to displace BERT-style encoders next.
  • A direct controlled comparison that keeps the same encoder backbone and varies only the pretraining objective (MLM versus text-to-text) would separate the architecture's contribution from BERT's head start in adoption.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. This manuscript surveys relation extraction (RE) techniques based on language models, focusing on papers from ACL, NAACL, AACL, and EACL between 2020 and 2023. The authors report screening 81 conference papers, including 65 after exclusion criteria, and analyzing 56 datasets; the abstract and conclusion state that 137 papers were analyzed, combining dataset papers and research papers. The survey categorizes RE work by task (sentence-level, document-level, few-shot, distant supervision, open RE, multilingual/multimodal), tabulates the most frequent models and datasets, and compares state-of-the-art results on TACRED, DocRED, and FewRel. The central claims are that BERT-based methods dominate state-of-the-art RE results and that LLMs such as T5 show promise in few-shot RE, especially for unseen relations.

Significance. If the survey's conclusions are correct, the paper provides a useful consolidation of RE research at ACL venues over a focused four-year window, with detailed tables of models, datasets, and benchmark results. The compilation of 65 papers and 56 datasets is a potentially valuable reference for practitioners, and the observation that BERT/RoBERTa occupy most top benchmark slots (Tables 5-7) is a falsifiable, clearly stated claim. The normalization-by-release-year analysis in Section 7.2 is a thoughtful attempt to address the confounding factor that BERT is older than T5 or GPT. However, because the survey's corpus is restricted to four ACL venues and the inclusion criteria are subjective, the generalizability of the dominance claim to the broader RE field is not established. The paper's most serious weakness is that its own tables contradict the few-shot LLM claim in the abstract, as detailed below.

major comments (4)
  1. [Abstract and Section 7.4] The abstract's claim that LLMs like T5 'excel in few-shot relation extraction scenarios where they excel in identifying previously unseen relations' is not supported by the paper's own evidence. In Table 7, the T5-based OffMML-G(+negs) ranks fourth on FewRel zero-shot RE with F1=61.3, behind BERT-based ESC-ZSRE (81.68), GPT2+BART-based RelationPrompt (79.96), and BERT-based IDL (62.61). Section 7.4 also concedes that LLMs 'do not play a central role in advancing state-of-the-art performance in extraction tasks.' The few-shot excellence claim should be removed or substantially weakened to match the data.
  2. [Section 2, Table 1, and Conclusion] The paper reports inconsistent corpus sizes: the abstract and conclusion state '137 papers,' while Section 2 reports 'we examined 81 papers' and 'the final set comprised 65 papers.' Table 1 clarifies that 137 = 56 dataset papers + 81 research papers, but this arithmetic is not explained in the text and the abstract's 'analyzing 137 papers' conflates dataset papers with the survey's core analysis. The methodology should state explicitly that 81 research papers were screened, 65 were included, and 56 datasets were catalogued separately.
  3. [Section 2 and Section 6] The inclusion criteria are applied subjectively ('papers that presented novel approaches or significant advancements,' exclusion of temporal RE, NER-focused, non-LM, and non-adopted encoding methods), and the survey does not validate the representativeness of the resulting corpus against any external source. Since the central dominance conclusion in Section 6 is based only on the top-five entries per benchmark from this filtered set, the conclusion may reflect venue and selection bias. I recommend adding a sensitivity analysis or at least an explicit discussion of how the exclusion criteria could affect the BERT-dominance and LLM findings.
  4. [Tables 5-7 and Section 6] The comparison in Section 6 is based on only three benchmarks (TACRED, DocRED, FewRel) and only the top five systems per benchmark, with no pooling of runs or statistical tests. The statement that 'BERT and RoBERTa collectively represent a substantial 75% of the primary outcomes' is arithmetically unclear: in the 15 total slots across Tables 5-7, BERT/RoBERTa appear in 11 slots (73%), and the 'primary outcomes' are not defined. The dominance conclusion would be more robust if the authors reported how many of the 65 included papers used BERT/RoBERTa versus T5/GPT in the actual benchmark evaluations, rather than only the top-five lists.
minor comments (6)
  1. [Section 2] The sentence 'To narrow down the papers, we searched for pieces that contained the phrase “Relation Extraction” in either the title or abstract' should specify whether the search was case-sensitive and whether 'relation extraction' as a phrase or as separate words was used, since this affects reproducibility.
  2. [Section 3.1] There is a typo: 'the entity pair‘ ‘Steve Jobs”' should read 'the entity pair “Steve Jobs” and “Apple Inc.”'.
  3. [Section 5.1] The paragraph on [163] states 'the model attained state-of-the-art results on benchmark datasets, namely TACRED and SemEval 2010,' but the reference list shows this is an AACL 2022 paper; the acronym 'FPC' in the same paragraph is not defined at first use.
  4. [Section 6] The sentence 'Large language models like RoBERTa and BERT are widely used' is misleading because RoBERTa and BERT are not typically classified as large language models in the sense of GPT or T5; consider revising to 'encoder-only language models like RoBERTa and BERT.'
  5. [Section 7.2] In Table 8, the count for BERT base is listed as 45, but the reference list contains 46 entries; please double-check the count and the alignment of references.
  6. [Section 8] The phrase 'they excel in identifying previously unseen relations' in the conclusion repeats the unsupported few-shot claim; see the major comment above.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the survey's conclusions are descriptive tallies of its explicitly selected corpus, and the only author-overlapping citation is non-load-bearing.

full rationale

This paper is a descriptive literature survey, not a derivation with fitted parameters or equations; its central claims (BERT/RoBERTa dominance and LLM few-shot promise) are summaries of the 65 selected papers and of three benchmark leaderboard tables it compiles (Tables 5-7). The only author-overlapping reference is [24], cited in the introduction as an example of NLP applied to social media; it is not used to justify the survey's methodology, inclusion criteria, or conclusions, so it does not constitute load-bearing self-citation. The conclusion that BERT-based models dominate is a direct tally of the selected corpus (Section 6), and the paper itself notes the corpus is restricted to ACL, NAACL, AACL, and EACL 2020-2023 with stated inclusion and exclusion criteria; any representativeness limitation is a scope and selection concern, not a circular reduction. The skeptical observation that Table 7 shows T5 ranking fourth on FewRel while the abstract says T5 'excels' is an internal-evidence inconsistency, but inconsistency is not circularity: the few-shot claim is not defined in terms of the tabulated result, and no parameter is fitted to make it true. No step in the paper reduces by construction to its own inputs.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The survey introduces no free parameters or invented entities. It depends on domain assumptions about the representativeness of the selected venues and search strategy, and on the accuracy of the underlying papers it summarizes.

assumptions (4)
  • domain assumption The ACL conference family (ACL, NAACL, AACL, EACL) is representative of cutting-edge relation extraction research.
    The survey restricts its corpus to these venues, potentially missing relevant work at EMNLP, ICLR, NeurIPS, or workshops.
  • domain assumption Searching for the phrase 'Relation Extraction' in title or abstract captures all relevant RE papers.
    Papers using other terminology (e.g., 'relation classification', 'joint entity and relation extraction') might be missed.
  • domain assumption The reported results from the surveyed papers are accurate.
    The survey aggregates F1 scores and model usage without independent verification.
  • ad hoc to paper The inclusion and exclusion criteria defined by the authors (excluding temporal RE, NER-focused, non-LM, and non-adopted encoding methods) are appropriate for mapping the field.
    These exclusions are chosen by the authors and may bias the trend analysis toward BERT-based methods.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A survey on cutting-edge relation extraction techniques based on language models." pith.science (2026). https://pith.science/paper/BQ6K5K3D

@misc{pith2026241118157,
  author       = {Pith},
  title        = {Pith review of: A survey on cutting-edge relation extraction techniques based on language models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BQ6K5K3D}},
  note         = {Machine review of arXiv:2411.18157}
}
read the original abstract

This comprehensive survey delves into the latest advancements in Relation Extraction (RE), a pivotal task in natural language processing essential for applications across biomedical, financial, and legal sectors. This study highlights the evolution and current state of RE techniques by analyzing 137 papers presented at the Association for Computational Linguistics (ACL) conferences over the past four years, focusing on models that leverage language models. Our findings underscore the dominance of BERT-based methods in achieving state-of-the-art results for RE while also noting the promising capabilities of emerging large language models (LLMs) like T5, especially in few-shot relation extraction scenarios where they excel in identifying previously unseen relations.

Figures

Figures reproduced from arXiv: 2411.18157 by the authors.

Figure 1
Figure 1. Graphic explanation of our inclusion/exclusion criteria 3 Background This section focuses on the theoretical principles that form the basis of our survey. We aim to provide the reader with the necessary conceptual foundations for RE and Language Models. 3.1 Relation Extraction RE is a task in natural language processing that aims to identify and classify the relationships between entities mentioned in the text. This… view at source ↗
Figure 2
Figure 2. Trend and Distribution of Publications Over the Years In terms of application areas, our analysis reveals that relation extraction has found application in diverse domains, including media, academia, economics, and even unconventional realms such as heritage conservation and mathematics. This broad applicability underscores the technique’s usefulness and emphasizes the need for new systems capable of autonomously id… view at source ↗
Figure 3
Figure 3. Trend and Distribution of Publications Over the Years and conferences In the upcoming subsections, we lay the groundwork for addressing current challenges while providing an exhaustive review of language models used for RE. Our approach is to align our findings with our RQ in a concerted effort to contribute meaningfully to ongoing research in the field of RE. 7.1 RQ1: What are the challenges of RE that are being so… view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DocIE@XLLM25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations

    cs.CL 2025-07 conditional novelty 4.0 of 10

    A retrieval-based in-context learning pipeline built on synthetic demonstrations achieves only modest entity and relation extraction scores in zero-shot document-level information extraction.

Reference graph

Works this paper leans on

160 extracted references · 47 canonical work pages · cited by 1 Pith paper

  1. [1]

    In: Proceedings of the second workshop on EVENTS: Definition, detection, coreference, and representation

    Aguilar, J., Beller, C., McNamee, P., Van Durme, B., Strassel, S., Song, Z., Ellis, J.: A comparison of the events and relations across ace, ere, tac-kbp, and framenet annotation standards. In: Proceedings of the second workshop on EVENTS: Definition, detection, coreference, and representation. pp. 45–53 (2014)

    2014

  2. [3]

    arXiv preprint arXiv:2004.14855 (2020) 38 Diaz-Garcia and Amador Diaz Lopez

    Alt, C., Gabryszak, A., Hennig, L.: Tacred revisited: A thorough evaluation of the tacred relation extraction task. arXiv preprint arXiv:2004.14855 (2020) 38 Diaz-Garcia and Amador Diaz Lopez

    arXiv 2020

  3. [4]

    In: Proceedings of the 57th Annual Meeting of the Associa- tion for Computational Linguistics

    Amini, M.R., Kananizadeh, N., Ganjtabesh, M.: Mathqa: Towards interpretable math word problem solving with operation-based formalisms. In: Proceedings of the 57th Annual Meeting of the Associa- tion for Computational Linguistics. pp. 6196–6206 (2019)

    2019

  4. [5]

    In: Hämäläinen, M., Alnajjar, K., Partanen, N., Rueter, J

    Arcan, M., O’Halloran, R., Robin, C., Buitelaar, P.: Towards bootstrapping a chatbot on industrial heritage through term and relation extraction. In: Hämäläinen, M., Alnajjar, K., Partanen, N., Rueter, J. (eds.) Proceedings of the 2nd International Workshop on Natural Language Processing for Digi- tal Humanities. pp. 108–122. Association for Computational...

    2022

  5. [6]

    Sensors 22(13), 4911 (2022)

    Baek, H.R., Choi, Y.S.: Enhancing targeted minority class prediction in sentence-level relation extrac- tion. Sensors 22(13), 4911 (2022)

    2022

  6. [7]

    In: Louvan, S., Madotto, A., Madureira, B

    Bassignana, E., Plank, B.: What do you mean by relation extraction? a survey on datasets and study on scientific relation classification. In: Louvan, S., Madotto, A., Madureira, B. (eds.) Pro- ceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Re- search Workshop. pp. 67–83. Association for Computational Linguist...

    doi:10.18653/v1/2022.acl-srw.7 2022

  7. [8]

    Nature ge- netics 36(5), 431–432 (2004)

    Becker, K.G., Barnes, K.C., Bright, T.J., Wang, S.A.: The genetic association database. Nature ge- netics 36(5), 431–432 (2004)

    2004

  8. [9]

    arXiv preprint arXiv:1903.10676 (2019)

    Beltagy, I., Lo, K., Cohan, A.: Scibert: A pretrained language model for scientific text. arXiv preprint arXiv:1903.10676 (2019)

    arXiv 2019

Show all 160 references
  1. [10]

    In: Advances in Neural Information Processing Systems

    Bengio, Y., Ducharme, R., Vincent, P.: A neural probabilistic language model. In: Advances in Neural Information Processing Systems. vol. 3, pp. 1137–1155 (2001)

    2001

  2. [11]

    BMC bioinformatics16, 1–17 (2015)

    Bravo, À., Piñero, J., Queralt-Rosinach, N., Rautschka, M., Furlong, L.I.: Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research. BMC bioinformatics16, 1–17 (2015)

    2015

  3. [12]

    In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t

    Brody, S., Wu, S., Benton, A.: Towards realistic few-shot relation extraction. In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t. (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. pp. 5338–5345. Association for Computational Lingui...

    2021 doi

  4. [13]

    Cai, P.X., Fan, Y.C., Leu, F.Y.: Compare encoder-decoder, encoder-only, and decoder-only architec- tures for text generation on low-resource datasets. In: Advances on Broad-Band Wireless Computing, Communication and Applications: Proceedings of the 16th International Conferenc...

    2022

  5. [14]

    In: Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tur, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y

    Chen, C.Y., Li, C.T.: ZS-BERT: Towards zero-shot relation extraction with attribute represen- tation learning. In: Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tur, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y. (eds.) Proceedings of the 2021 ...

    2021

  6. [15]

    In: Rogers, A., Boyd-Graber, J., Okazaki, N

    Chen, H., Chen, B., Zhou, X.: Did the models understand documents? benchmarking models for language understanding in document-level relation extraction. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computatio...

    2023 doi

  7. [16]

    In: Wu, D., Carpuat, M., Carreras, X., Vecchi, E.M

    Cho, K., van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder–decoder approaches. In: Wu, D., Carpuat, M., Carreras, X., Vecchi, E.M. (eds.) Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statisti...

    2014 doi

  8. [17]

    Christopoulou, F., Miwa, M., Ananiadou, S.: Distantly supervised relation extraction with sentence reconstruction and knowledge base priors. In: Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani- Tur,D.,Beltagy,I.,Bethard,S.,Cotterell,R.,Chakraborty,T.,Zhou,Y.(eds.)Procee...

    2021

  9. [18]

    Biomedical informatics insights10, 1178222618792860 (2018)

    Coppersmith, G., Leary, R., Crutchley, P., Fine, A.: Natural language processing of social media as screening for suicide risk. Biomedical informatics insights10, 1178222618792860 (2018)

    2018

  10. [19]

    In: Zong, C., Xia, F., Li, W., Navigli, R

    Cui, L., Yang, D., Yu, J., Hu, C., Cheng, J., Yi, J., Xiao, Y.: Refining sample embeddings with relation prototypes to enhance continual relation extraction. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Proceedings of the 59th Annual Meeting of the Association for Computa...

    2021 doi

  11. [20]

    arXiv preprint arXiv:2102.06540 (2021)

    Dai, Q., Inoue, N., Takahashi, R., Inui, K.: Two training strategies for improving relation extraction over universal graph. arXiv preprint arXiv:2102.06540 (2021)

    2021 arXiv

  12. [21]

    In: Merlo, P., Tiedemann, J., Tsarfaty, R

    Dai, Q., Inoue, N., Takahashi, R., Inui, K.: Two training strategies for improving relation ex- traction over universal graph. In: Merlo, P., Tiedemann, J., Tsarfaty, R. (eds.) Proceedings of the 16th Conference of the European Chapter of the Association for Computational Ling...

    2021 doi

  13. [22]

    In: Precup, D., Teh, Y.W

    Dauphin, Y.N., Fan, A., Auli, M., Grangier, D.: Language modeling with gated convolutional networks. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learn- ing. Proceedings of Machine Learning Research, vol. 70, pp. 933–941. PMLR, I...

    2017

  14. [23]

    Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Trans- formers for Language Understanding (oct 2018),http://arxiv.org/abs/1810.04805

    2018 arXiv

  15. [24]

    International Journal of Computational Intelligence Systems17(1), 93 (2024)

    Diaz-Garcia, J.A., Gutiérrez-Batista, K., Fernandez-Basso, C., Ruiz, M.D., Martin-Bautista, M.J.: A flexible big data system for credibility-based filtering of social media information according to expertise. International Journal of Computational Intelligence Systems17(1), 93 (2024)

    2024

  16. [25]

    arXiv preprint arXiv:2407.21783 (2024)

    Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al.: The llama 3 herd of models. arXiv preprint arXiv:2407.21783 (2024)

    2024 arXiv

  17. [26]

    In: Merlo, P., Tiedemann, J., Tsarfaty, R

    Eberts, M., Ulges, A.: An end-to-end model for entity-level relation extraction using multi-instance learning. In: Merlo, P., Tiedemann, J., Tsarfaty, R. (eds.) Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Vo...

    2021 doi

  18. [27]

    In: Merlo, P., Tiedemann, J., Tsarfaty, R

    Eyal, M., Amrami, A., Taub-Tabib, H., Goldberg, Y.: Bootstrapping relation extractors using syntactic search by examples. In: Merlo, P., Tiedemann, J., Tsarfaty, R. (eds.) Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguis- ...

    2021 doi

  19. [28]

    In: Merlo, P., Tiedemann, J., Tsarfaty, R

    Fu, L., Grishman, R.: Learning relatedness between types with prototypes for relation extraction. In: Merlo, P., Tiedemann, J., Tsarfaty, R. (eds.) Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. pp. 201...

    2011 doi

  20. [29]

    In: 55th annual meeting of the Association for Computational Linguistics (ACL) (2017) 40 Diaz-Garcia and Amador Diaz Lopez

    Gardent, C., Shimorina, A., Narayan, S., Perez-Beltrachini, L.: Creating training corpora for nlg micro- planning. In: 55th annual meeting of the Association for Computational Linguistics (ACL) (2017) 40 Diaz-Garcia and Amador Diaz Lopez

    2017

  21. [30]

    ACM Trans- actions on Computing for Healthcare (HEALTH)3(1), 1–23 (2021)

    Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., Poon, H.: Domain-specific language model pretraining for biomedical natural language processing. ACM Trans- actions on Computing for Healthcare (HEALTH)3(1), 1–23 (2021)

    2021

  22. [32]

    Journal of biomedical informatics45(5), 885–892 (2012)

    Gurulingappa, H., Rajput, A.M., Roberts, A., Fluck, J., Hofmann-Apitius, M., Toldo, L.: Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports. Journal of biomedical informatics45(5), 885–892 (2012)

    2012

  23. [33]

    In: Rogers, A., Boyd-Graber, J., Okazaki, N

    Gururaja, S., Dutt, R., Liao, T., Rosé, C.: Linguistic representations for fewer-shot relation ex- traction across domains. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Lo...

    2023 doi

  24. [34]

    In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t

    Han, J., Cheng, B., Lu, W.: Exploring task difficulty for few-shot relation extraction. In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t. (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. pp. 2605–2616. Association for Computation...

    2021 doi

  25. [35]

    In: Goldberg,Y.,Kozareva,Z.,Zhang,Y.(eds.)FindingsoftheAssociationforComputationalLinguistics: EMNLP 2022

    Han, J., Zhao, S., Cheng, B., Ma, S., Lu, W.: Generative prompt tuning for relation classification. In: Goldberg,Y.,Kozareva,Z.,Zhang,Y.(eds.)FindingsoftheAssociationforComputationalLinguistics: EMNLP 2022. pp. 3170–3185. Association for Computational Linguistics, Abu Dhabi, U...

    2022 doi

  26. [36]

    In: Proceedings of the AAAI conference on artificial intelligence

    Han, X., Sun, L.: Global distant supervision for relation extraction. In: Proceedings of the AAAI conference on artificial intelligence. vol. 30 (2016)

    2016

  27. [37]

    In: Wong, K.F., Knight, K., Wu, H

    Han, X., Gao, T., Lin, Y., Peng, H., Yang, Y., Xiao, C., Liu, Z., Li, P., Zhou, J., Sun, M.: More data, more relations, more context and more openness: A review and outlook for relation extraction. In: Wong, K.F., Knight, K., Wu, H. (eds.) Proceedings of the 1st Conference of ...

    2020

  28. [38]

    arXiv preprint arXiv:1909.13078 (2019)

    Han, X., Gao, T., Yao, Y., Ye, D., Liu, Z., Sun, M.: Opennre: An open and extensible toolkit for neural relation extraction. arXiv preprint arXiv:1909.13078 (2019)

    2019 arXiv

  29. [39]

    In: Riloff, E., Chiang, D., Hockenmaier, J., Tsujii, J

    Han, X., Zhu, H., Yu, P., Wang, Z., Yao, Y., Liu, Z., Sun, M.: FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. In: Riloff, E., Chiang, D., Hockenmaier, J., Tsujii, J. (eds.) Proceedings of the 2018 Conference on Empir...

    2018 doi

  30. [40]

    In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t

    Hao, K., Yu, B., Hu, W.: Knowing false negatives: An adversarial training method for distantly su- pervised relation extraction. In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t. (eds.) Proceed- ings of the 2021 Conference on Empirical Methods in Natural Language Processing....

    2021

  31. [41]

    arXiv preprint arXiv:1911.10422 (2019) A survey on cutting-edge relation extraction 41

    Hendrickx, I., Kim, S.N., Kozareva, Z., Nakov, P., Séaghdha, D.O., Padó, S., Pennacchiotti, M., Ro- mano, L., Szpakowicz, S.: Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals. arXiv preprint arXiv:1911.10422 (2019) A survey on cutti...

    2019 arXiv

  32. [42]

    In: Rogers, A., Boyd-Graber, J., Okazaki, N

    Hennig, L., Thomas, P., Möller, S.: MultiTACRED: A multilingual version of the TAC rela- tion extraction dataset. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pa- per...

    2023 doi

  33. [43]

    Computers in biology and medicine155, 106649 (2023)

    Hossain, E., Rana, R., Higgins, N., Soar, J., Barua, P.D., Pisani, A.R., Turner, K.: Natural language processing in electronic health records in relation to healthcare decision-making: a systematic review. Computers in biology and medicine155, 106649 (2023)

    2023

  34. [44]

    In: Rogers, A., Boyd-Graber, J., Okazaki, N

    Hu, X., Guo, Z., Teng, Z., King, I., Yu, P.S.: Multimodal relation extraction with cross-modal retrieval and synthesis. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short ...

    2023 doi

  35. [45]

    In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t

    Hu, X., Zhang, C., Ma, F., Liu, C., Wen, L., Yu, P.S.: Semi-supervised relation extraction via incre- mental meta self-training. In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2021. pp. 487–496. Assoc...

    2021 doi

  36. [46]

    In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t

    Hu, X., Zhang, C., Yang, Y., Li, X., Lin, L., Wen, L., Yu, P.S.: Gradient imitation reinforcement learning for low resource relation extraction. In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t. (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Langua...

    2021 doi

  37. [47]

    In: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V

    Huang, J.Y., Li, B., Xu, J., Chen, M.: Unified semantic typing with meaningful label inference. In: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V. (eds.) Proceedings of the 2022 Confer- ence of the North American Chapter of the Association for Computational Linguistics: Human...

    2022 doi

  38. [48]

    In: Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)

    Huang, K., Qi, P., Wang, G., Ma, T., Huang, J.: Entity and evidence guided document-level relation extraction. In: Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021). pp. 307–315. Association for Computational Linguistics, Online (Aug 2021). htt...

    2021 doi

  39. [49]

    Huang, Q., Zhu, S., Feng, Y., Ye, Y., Lai, Y., Zhao, D.: Three sentences are all you need: Local path enhanced document relation extraction. In: Proceedings of the 59th Annual Meeting of the Associa- tion for Computational Linguistics and the 11th International Joint Conferenc...

    2021 doi

  40. [50]

    In: Rogers, A., Boyd-Graber, J., Okazaki, N

    Huguet Cabot, â.L., Tedeschi, S., Ngonga Ngomo, A.C., Navigli, R.: REDfm: a filtered and mul- tilingual relation extraction dataset. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceed- ings of the 61st Annual Meeting of the Association for Computational Linguistics (V...

    2023 doi

  41. [51]

    arXiv preprint arXiv:1804.06987 (2018)

    Jat, S., Khandelwal, S., Talukdar, P.: Improving distantly supervised relation extraction using word and entity based attention. arXiv preprint arXiv:1804.06987 (2018)

    2018 arXiv

  42. [52]

    In: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)

    Jaume, G., Ekenel, H.K., Thiran, J.P.: Funsd: A dataset for form understanding in noisy scanned documents. In: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW). vol. 2, pp. 1–6. IEEE (2019) 42 Diaz-Garcia and Amador Diaz Lopez

    2019

  43. [53]

    Jiang, Y., Zaporojets, K., Deleu, J., Demeester, T., Develder, C.: Recipe instruction semantics corpus (risec): Resolving semantic structure and zero anaphora in recipes. In: Proceedings of the 1st Con- ference of the Asia-Pacific Chapter of the Association for Computational L...

    2020

  44. [55]

    Transactions of the association for computational linguistics8, 64–77 (2020)

    Joshi, M., Chen, D., Liu, Y., Weld, D.S., Zettlemoyer, L., Levy, O.: Spanbert: Improving pre-training by representing and predicting spans. Transactions of the association for computational linguistics8, 64–77 (2020)

    2020

  45. [56]

    In: Erk, K., Smith, N.A

    Kadlec, R., Schmid, M., Bajgar, O., Kleindienst, J.: Text understanding with the attention sum reader network. In: Erk, K., Smith, N.A. (eds.) Proceedings of the 54th Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers). pp. 908–918. Associ...

    2016 doi

  46. [57]

    In: Proceedings of the 18th BioNLP Workshop and Shared Task

    Khachatrian, H., Nersisyan, L., Hambardzumyan, K., Galstyan, T., Hakobyan, A., Arakelyan, A., Rzhetsky, A., Galstyan, A.: BioRelEx 1.0: Biological relation extraction benchmark. In: Proceedings of the 18th BioNLP Workshop and Shared Task. pp. 176–190. Association for Computati...

    2019 doi

  47. [58]

    Nucleic acids research41(D1), D464–D469 (2012)

    Kim Kjærulff, S., Wich, L., Kringelum, J., Jacobsen, U.P., Kouskoumvekaki, I., Audouze, K., Lund, O., Brunak, S., Oprea, T.I., Taboureau, O.: Chemprot-2.0: visual navigation in a disease chemical biology database. Nucleic acids research41(D1), D464–D469 (2012)

    2012

  48. [59]

    In: Pro- ceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

    Koncel-Kedziorski, R., Roy, S., Wang, Y.: Parsing algebraic word problems into equations. In: Pro- ceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 2237–2247 (2016)

    2016

  49. [60]

    In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

    Kruiper, R., Vincent, J., Chen-Burger, J., Desmulliez, M., Konstas, I.: In layman’s terms: Semi-open relation extraction from scientific texts. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 1489–1500. Association for Computati...

    2020 doi

  50. [61]

    BMC medical informatics and decision making 21, 1–12 (2021)

    Lan, Y., He, S., Liu, K., Zeng, X., Liu, S., Zhao, J.: Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion. BMC medical informatics and decision making 21, 1–12 (2021)

    2021

  51. [62]

    In: Riloff, E., Chiang, D., Hockenmaier, J., Tsujii, J

    Lebanoff, L., Song, K., Liu, F.: Adapting the neural encoder-decoder framework from single to multi-document summarization. In: Riloff, E., Chiang, D., Hockenmaier, J., Tsujii, J. (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. pp...

    2018 doi

  52. [63]

    Bioinformatics36(4), 1234–1240 (2020)

    Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C.H., Kang, J.: Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics36(4), 1234–1240 (2020)

    2020

  53. [64]

    In: Levy, R., Specia, L

    Levy, O., Seo, M., Choi, E., Zettlemoyer, L.: Zero-shot relation extraction via reading comprehension. In: Levy, R., Specia, L. (eds.) Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017). pp. 333–342. Association for Computational Linguis...

    2017 doi

  54. [65]

    Li, J., Sun, Y., Johnson, R.J., Sciaky, D., Wei, C.H., Leaman, R., Davis, A.P., Mattingly, C.J., Wiegers, T.C.,Lu,Z.:Biocreativevcdrtaskcorpus:aresourceforchemicaldiseaserelationextraction.Database 2016 (2016) A survey on cutting-edge relation extraction 43

    2016

  55. [66]

    In: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V

    Liang, X., Wu, S., Li, M., Li, Z.: Modeling multi-granularity hierarchical features for relation ex- traction. In: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V. (eds.) Proceedings of the 2022 Con- ference of the North American Chapter of the Association for Computational Lin...

    2022 doi

  56. [69]

    arXiv preprint arXiv:1907.11692 (2019)

    Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)

    2019 arXiv

  57. [70]

    In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

    Lu, D., Neves, L., Carvalho, V., Zhang, N., Ji, H.: Visual attention model for name tagging in multi- modal social media. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 1990–1999 (2018)

    2018

  58. [71]

    In: Riloff, E., Chiang, D., Hockenmaier, J., Tsujii, J

    Luan, Y., He, L., Ostendorf, M., Hajishirzi, H.: Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction. In: Riloff, E., Chiang, D., Hockenmaier, J., Tsujii, J. (eds.) Proceedings of the 2018 Conference on Empirical Method...

    2018 doi

  59. [72]

    Briefings in Bioinformatics23(5), bbac282 (2022)

    Luo, L., Lai, P.T., Wei, C.H., Arighi, C.N., Lu, Z.: Biored: a rich biomedical relation extraction dataset. Briefings in Bioinformatics23(5), bbac282 (2022)

    2022

  60. [73]

    Briefings in bioinformatics23(6), bbac409 (2022)

    Luo, R., Sun, L., Xia, Y., Qin, T., Zhang, S., Poon, H., Liu, T.Y.: Biogpt: generative pre-trained transformer for biomedical text generation and mining. Briefings in bioinformatics23(6), bbac409 (2022)

    2022

  61. [74]

    In: Zong, C., Xia, F., Li, W., Navigli, R

    Lyu, S., Chen, H.: Relation classification with entity type restriction. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Findings of the Association for Computational Linguistics: ACL- IJCNLP 2021. pp. 390–395. Association for Computational Linguistics, Online (Aug 2021). ht...

    2021 doi

  62. [75]

    In: Zong, C., Xia, F., Li, W., Navigli, R

    Ma, R., Gui, T., Li, L., Zhang, Q., Huang, X., Zhou, Y.: SENT: Sentence-level distant relation ex- traction via negative training. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the ...

    2021 doi

  63. [76]

    Ma, Y., Wang, A., Okazaki, N.: DREEAM: Guiding attention with evidence for improving document- levelrelationextraction.In:Vlachos,A.,Augenstein,I.(eds.)Proceedingsofthe17thConferenceofthe European Chapter of the Association for Computational Linguistics. pp. 1971–1983. Associa...

    1971 doi

  64. [77]

    Mathur, P., Jain, R., Dernoncourt, F., Morariu, V., Tran, Q.H., Manocha, D.: Timers: document- level temporal relation extraction. In: Proceedings of the 59th Annual Meeting of the Association 44 Diaz-Garcia and Amador Diaz Lopez for Computational Linguistics and the 11th Inte...

    2021

  65. [78]

    In: 2012 IEEE Workshop on Spoken Language Technology, SLT 2012 - Proceedings

    Mikolov, T., Zweig, G.: Context dependent recurrent neural network language model. In: 2012 IEEE Workshop on Spoken Language Technology, SLT 2012 - Proceedings. pp. 234–239. IEEExplore (feb 2012). https://doi.org/10.1109/SLT.2012.6424228

    2012

  66. [79]

    In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

    Mirza, P., Tonelli, S.: An analysis of causality between events and its relation to temporal information. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. pp. 2097–2106 (2014)

    2014

  67. [80]

    Mostafazadeh, N., Grealish, A., Chambers, N., Allen, J., Vanderwende, L.: Caters: Causal and tempo- ralrelationschemeforsemanticannotationofeventstructures.In:ProceedingsoftheFourthWorkshop on Events. pp. 51–61 (2016)

    2016

  68. [81]

    arXiv preprint arXiv:1905.06939 (2019)

    Mysore, S., Jensen, Z., Kim, E., Huang, K., Chang, H.S., Strubell, E., Flanigan, J., McCallum, A., Olivetti, E.: The materials science procedural text corpus: Annotating materials synthesis procedures with shallow semantic structures. arXiv preprint arXiv:1905.06939 (2019)

    2019 arXiv

  69. [82]

    In: Vlachos, A., Augenstein, I

    Najafi, S., Fyshe, A.: Weakly-supervised questions for zero-shot relation extraction. In: Vlachos, A., Augenstein, I. (eds.) Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. pp. 3075–3087. Association for Computationa...

    2023 doi

  70. [83]

    In: Proceedings of the 58th Annual Meeting of the Association for Com- putational Linguistics

    Nan, G., Guo, Z., Sekulic, I., Lu, W.: Reasoning with latent structure refinement for document- level relation extraction. In: Proceedings of the 58th Annual Meeting of the Association for Com- putational Linguistics. pp. 1546–1557. Association for Computational Linguistics, O...

    2020 doi

  71. [84]

    Applied Intelligence 53(9), 10602–10635 (2023)

    Nassiri, K., Akhloufi, M.: Transformer models used for text-based question answering systems. Applied Intelligence 53(9), 10602–10635 (2023)

    2023

  72. [85]

    arXiv preprint arXiv:1804.07828 (2018)

    Ning, Q., Wu, H., Roth, D.: A multi-axis annotation scheme for event temporal relations. arXiv preprint arXiv:1804.07828 (2018)

    2018 arXiv

  73. [86]

    OpenAI, A.J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al.: Gpt-4 technical report. 2023. arXiv preprint arXiv:2303.08774

    2023 arXiv

  74. [87]

    In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

    Parikh, A., Täckström, O., Das, D., Uszkoreit, J.: A decomposable attention model for natural lan- guage inference. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. pp. 2249–2255. Association for Computational Linguistics, Austin, Tex...

    2016 doi

  75. [88]

    In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

    Patel, S., Khashabi, D., Roth, D.: Svamp: Structural variations for assessing mathematical problem- solving. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 305–316 (2021)

    2021

  76. [89]

    In: Demner-Fushman, D., Cohen, K.B., Ananiadou, S., Tsujii, J

    Peng, Y., Yan, S., Lu, Z.: Transfer learning in biomedical natural language processing: An evaluation of BERT and ELMo on ten benchmarking datasets. In: Demner-Fushman, D., Cohen, K.B., Ananiadou, S., Tsujii, J. (eds.) Proceedings of the 18th BioNLP Workshop and Shared Task. p...

    2019 doi

  77. [90]

    In: Carpuat, M., de Marn- effe, M.C., Meza Ruiz, I.V

    Popovic, N., Färber, M.: Few-shot document-level relation extraction. In: Carpuat, M., de Marn- effe, M.C., Meza Ruiz, I.V. (eds.) Proceedings of the 2022 Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Tech- nologies...

    2022 doi

  78. [92]

    In: Muresan, S., Nakov, P., Villavicencio, A

    Qin,C.,Joty,S.:Continualfew-shotrelationlearningviaembeddingspaceregularizationanddataaug- mentation. In: Muresan, S., Nakov, P., Villavicencio, A. (eds.) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 2776–...

    2022 doi

  79. [93]

    Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training (2018)

    2018

  80. [94]

    The Journal of Machine Learning Research 21(1), 5485–5551 (2020)

    Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Ex- ploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research 21(1), 5485–5551 (2020)

    2020

  81. [95]

    International Journal of Computer Applications134(8), 6–9 (2016)

    Ranjan, N., Mundada, K., Phaltane, K., Ahmad, S.: A survey on techniques in nlp. International Journal of Computer Applications134(8), 6–9 (2016)

    2016

  82. [96]

    arXiv preprint arXiv:1908.10084 (2019)

    Reimers, N., Gurevych, I.: Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084 (2019)

    2019 arXiv

  83. [97]

    In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part III 21

    Riedel, S., Yao, L., McCallum, A.: Modeling relations and their mentions without labeled text. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part III 21. pp. 148–163. Spring...

    2010

  84. [98]

    In: Pro- ceedings of the 2021 conference on empirical methods in natural language processing

    Roy, A., Pan, S.: Incorporating medical knowledge in bert for clinical relation extraction. In: Pro- ceedings of the 2021 conference on empirical methods in natural language processing. pp. 5357–5366 (2021)

    2021

  85. [99]

    In: Merlo, P., Tiedemann, J., Tsarfaty, R

    Seganti, A., Firląg, K., Skowronska, H., Satława, M., Andruszkiewicz, P.: Multilingual entity and relation extraction dataset and model. In: Merlo, P., Tiedemann, J., Tsarfaty, R. (eds.) Proceed- ings of the 16th Conference of the European Chapter of the Association for Comput...

    1946 doi

  86. [100]

    In: Proceed- ings of the 58th Annual Meeting of the Association for Computational Linguistics

    Shahbazi, H., Fern, X., Ghaeini, R., Tadepalli, P.: Relation extraction with explanation. In: Proceed- ings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 6488–6494. Association for Computational Linguistics, Online (Jul 2020). https://doi.org...

    2020 doi

  87. [101]

    In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t

    Sui, D., Chen, Y., Liu, K., Zhao, J.: Distantly supervised relation extraction in federated set- tings. In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2021. pp. 569–583. Association for Computational ...

    2021 doi

  88. [102]

    Journal of the Association for Information Science and Technology 73(2), 268–287 (2022)

    Suissa, O., Elmalech, A., Zhitomirsky-Geffet, M.: Text analysis using deep neural networks in digital humanities and information science. Journal of the Association for Information Science and Technology 73(2), 268–287 (2022)

    2022

  89. [103]

    In: 2018 International Conference on Asian Language Processing (IALP)

    Sun, P., Yang, X., Zhao, X., Wang, Z.: An overview of named entity recognition. In: 2018 International Conference on Asian Language Processing (IALP). pp. 273–278. IEEE (2018)

    2018

  90. [104]

    In: Rogers, A., Boyd-Graber, J., Okazaki, N

    Sun, Q., Huang, K., Yang, X., Hong, P., Zhang, K., Poria, S.: Uncertainty guided label denoising for document-level distant relation extraction. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Ling...

    2023 doi

  91. [105]

    In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

    Tan, Q., Xu, L., Bing, L., Ng, H.T., Aljunied, S.M.: Revisiting docred-addressing the false negative problem in relation extraction. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. pp. 8472–8487 (2022) 46 Diaz-Garcia and Amador Diaz Lopez

    2022

  92. [106]

    In: Vlachos, A., Augenstein, I

    Tan, X., Pergola, G., He, Y.: Event temporal relation extraction with Bayesian translational model. In: Vlachos, A., Augenstein, I. (eds.) Proceedings of the 17th Conference of the European Chap- ter of the Association for Computational Linguistics. pp. 1125–1138. Association ...

    2023 doi

  93. [107]

    arXiv preprint arXiv:2312.11805 (2023)

    Team, G., Anil, R., Borgeaud, S., Wu, Y., Alayrac, J.B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A.M., Hauth, A., et al.: Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805 (2023)

    2023 arXiv

  94. [108]

    In: Vlachos, A., Augenstein, I

    Teru, K.: Semi-supervised relation extraction via data augmentation and consistency-training. In: Vlachos, A., Augenstein, I. (eds.) Proceedings of the 17th Conference of the European Chap- ter of the Association for Computational Linguistics. pp. 1112–1124. Association for Co...

    2023 doi

  95. [109]

    Con- currency and Computation: Practice and Experience34(9), e6486 (2022)

    Tezgider, M., Yildiz, B., Aydin, G.: Text classification using improved bidirectional transformer. Con- currency and Computation: Practice and Experience34(9), e6486 (2022)

    2022

  96. [110]

    Knowledge-Based Systems251, 108956 (2022)

    Thomas, A., Sivanesan, S.: An adaptable, high-performance relation extraction system for complex sentences. Knowledge-Based Systems251, 108956 (2022)

    2022

  97. [111]

    In: Zong, C., Xia, F., Li, W., Navigli, R

    Tian, Y., Chen, G., Song, Y., Wan, X.: Dependency-driven relation extraction with attentive graph convolutional networks. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th Inte...

    2021 doi

  98. [112]

    In: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V

    Tiktinsky, A., Viswanathan, V., Niezni, D., Meron Azagury, D., Shamay, Y., Taub-Tabib, H., Hope, T., Goldberg, Y.: A dataset for n-ary relation extraction of drug combinations. In: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V. (eds.) Proceedings of the 2022 Conference of the...

    2022

  99. [113]

    In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003

    Tjong Kim Sang, E.F., De Meulder, F.: Introduction to the CoNLL-2003 shared task: Language- independent named entity recognition. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003. pp. 142–147 (2003),https://aclanthology.org/W03-0419

    2003

  100. [114]

    In: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V

    Vakil, N., Amiri, H.: Generic and trend-aware curriculum learning for relation extraction. In: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V. (eds.) Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Lan- g...

    2022 doi

  101. [115]

    In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R

    Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polo- sukhin, I.: Attention is All you Need. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Process-...

    2017

  102. [116]

    In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

    Veyseh, A.P.B., Dernoncourt, F., Dou, D., Nguyen, T.H.: Exploiting the syntax-model consistency for neural relation extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 8021–8032 (2020)

    2020

  103. [118]

    Linguistic Data Consortium, Philadelphia 57, 45 (2006)

    Walker, C., Strassel, S., Medero, J., Maeda, K.: Ace 2005 multilingual training corpus. Linguistic Data Consortium, Philadelphia 57, 45 (2006)

    2006

  104. [119]

    In: Muresan, S., Nakov, P., Villavicencio, A

    Wang, C., Liu, X., Chen, Z., Hong, H., Tang, J., Song, D.: DeepStruct: Pretraining of language models for structure prediction. In: Muresan, S., Nakov, P., Villavicencio, A. (eds.) Findings of the Association for Computational Linguistics: ACL 2022. pp. 803–823. Association fo...

    2022 doi

  105. [120]

    In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence

    Wang, S., Liu, C., Zhu, K.Q.: Deep neural solver for math word problems. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. pp. 1170–1176 (2017)

    2017

  106. [121]

    In: Zong, C., Xia, F., Li, W., Navigli, R

    Wang, Y., Sun, C., Wu, Y., Zhou, H., Li, L., Yan, J.: UniRE: A unified label space for entity re- lation extraction. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th Internati...

    2021 doi

  107. [122]

    In: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V

    Wang, Y., Chen, M., Zhou, W., Cai, Y., Liang, Y., Liu, D., Yang, B., Liu, J., Hooi, B.: Should we rely on entity mentions for relation extraction? debiasing relation extraction with counterfac- tual analysis. In: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V. (eds.) Proceedin...

    2022

  108. [123]

    Scientometrics pp

    de Winter, J.: Can chatgpt be used to predict citation counts, readership, and social media interaction? an exploration among 2222 scientific abstracts. Scientometrics pp. 1–19 (2024)

    2024

  109. [124]

    arXiv preprint arXiv:1910.03771 (2019)

    Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al.: Huggingface’s transformers: State-of-the-art natural language processing. arXiv preprint arXiv:1910.03771 (2019)

    2019 arXiv

  110. [125]

    In: Rogers, A., Boyd-Graber, J., Okazaki, N

    Wu, S., Fei, H., Cao, Y., Bing, L., Chua, T.S.: Information screening whilst exploiting! multi- modal relation extraction with feature denoising and multimodal topic modeling. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Ass...

    2023 doi

  111. [126]

    In: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V

    Xiao, Y., Zhang, Z., Mao, Y., Yang, C., Han, J.: SAIS: Supervising and augmenting intermediate steps for document-level relation extraction. In: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V. (eds.) Proceedings of the 2022 Conference of the North American Chapter of the Assoc...

    2022 doi

  112. [127]

    In: Zong, C., Xia, F., Li, W., Navigli, R

    Xie, C., Liang, J., Liu, J., Huang, C., Huang, W., Xiao, Y.: Revisiting the negative data of dis- tantly supervised relation extraction. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics an...

    2021 doi

  113. [128]

    In: Rogers, A., Boyd-Graber, J., Okazaki, N

    Xu, B., Wang, Q., Lyu, Y., Dai, D., Zhang, Y., Mao, Z.: S2ynRE: Two-stage self-training with syn- thetic data for low-resource relation extraction. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational L...

    2023 doi

  114. [129]

    (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pa- pers)

    Xu, J., Ma, M.D., Chen, M.: Can NLI provide proper indirect supervision for low-resource biomed- ical relation extraction? In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Lon...

    2023 doi

  115. [130]

    In: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V

    Xu, L., Choi, J.: Modeling task interactions in document-level joint entity and relation extrac- tion. In: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V. (eds.) Proceedings of the 2022 Con- ference of the North American Chapter of the Association for Computational Linguistics...

    2022 doi

  116. [131]

    In: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V

    Xu, W., Chen, K., Mou, L., Zhao, T.: Document-level relation extraction with sentences importance estimation and focusing. In: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V. (eds.) Proceedings of the 2022 Conference of the North American Chapter of the Association for Computa...

    2022 doi

  117. [132]

    In: Proceedings of the Twelfth Language Resources and Evaluation Conference

    Yamakata, Y., Mori, S., Carroll, J.A.: English recipe flow graph corpus. In: Proceedings of the Twelfth Language Resources and Evaluation Conference. pp. 5187–5194 (2020)

    2020

  118. [133]

    Yan,Z.,Jia,Z.,Tu,K.:Anempiricalstudyofpipelinevs.jointapproachestoentityandrelationextrac- tion.In:He,Y.,Ji,H.,Li,S.,Liu,Y.,Chang,C.H.(eds.)Proceedingsofthe2ndConferenceoftheAsia- Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint...

    2022

  119. [134]

    In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t

    Yan, Z., Zhang, C., Fu, J., Zhang, Q., Wei, Z.: A partition filter network for joint entity and relation extraction. In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t. (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. pp. 185–197. ...

    2021 doi

  120. [135]

    arXiv preprint arXiv:2106.02401 (2021)

    Yang, S., Zhang, Y., Niu, G., Zhao, Q., Pu, S.: Entity concept-enhanced few-shot relation extraction. arXiv preprint arXiv:2106.02401 (2021)

    2021 arXiv

  121. [136]

    In: Zong, C., Xia, F., Li, W., Navigli, R

    Yang, S., Zhang, Y., Niu, G., Zhao, Q., Pu, S.: Entity concept-enhanced few-shot relation ex- traction. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Proceedings of the 59th Annual Meet- ing of the Association for Computational Linguistics and the 11th International Joint ...

    2021 doi

  122. [137]

    In: He, Y., Ji, H., Li, S., Liu, Y., Chang, C.H

    Yang, S., Song, D.: FPC: Fine-tuning with prompt curriculum for relation extraction. In: He, Y., Ji, H., Li, S., Liu, Y., Chang, C.H. (eds.) Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th Internationa...

    2022

  123. [139]

    arXiv preprint arXiv:1909.03193 (2019) A survey on cutting-edge relation extraction 49

    Yao, L., Mao, C., Luo, Y.: Kg-bert: Bert for knowledge graph completion. arXiv preprint arXiv:1909.03193 (2019) A survey on cutting-edge relation extraction 49

    2019 arXiv

  124. [140]

    In: Proceedings of the 57th Annual Meeting of the As- sociation for Computational Linguistics

    Yao,Y.,Ye,D.,Li,P.,Han,X.,Lin,Y.,Liu,Z.,Liu,Z.,Huang,L.,Zhou,J.,Sun,M.:DocRED:Alarge- scale document-level relation extraction dataset. In: Proceedings of the 57th Annual Meeting of the As- sociation for Computational Linguistics. pp. 764–777. Association for Computational Lin...

    doi

  125. [141]

    In: Mure- san, S., Nakov, P., Villavicencio, A

    Ye, D., Lin, Y., Li, P., Sun, M.: Packed levitated marker for entity and relation extraction. In: Mure- san, S., Nakov, P., Villavicencio, A. (eds.) Proceedings of the 60th Annual Meeting of the Associa- tion for Computational Linguistics (Volume 1: Long Papers). pp. 4904–4917...

    2022 doi

  126. [142]

    In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

    Yu, D., Sun, K., Cardie, C., Yu, D.: Dialogue-based relation extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 4927–4940. Association for ComputationalLinguistics,Online(Jul2020).https://doi.org/10.18653/v1/2020.acl-ma...

    doi

  127. [143]

    In: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V

    Yu, J., Yang, D., Tian, S.: Relation-specific attentions over entity mentions for enhanced document- level relation extraction. In: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V. (eds.) Proceedings of the 2022 Conference of the North American Chapter of the Association for Co...

    2022 doi

  128. [144]

    In: Rogers, A., Boyd-Graber, J., Okazaki, N

    Yuan, C., Huang, H., Cao, Y., Wen, Y.: Discriminative reasoning with sparse event representation for document-level event-event relation extraction. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational ...

    2023 doi

  129. [145]

    In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t

    Yuan, C., Eldardiry, H.: Unsupervised relation extraction: A variational autoencoder approach. In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t. (eds.) Proceedings of the 2021 Con- ference on Empirical Methods in Natural Language Processing. pp. 1929–1938. Association for Co...

    2021 doi

  130. [146]

    Information Processing & Management58(4), 102563 (2021)

    Zaporojets, K., Deleu, J., Develder, C., Demeester, T.: Dwie: An entity-centric dataset for multi-task document-level information extraction. Information Processing & Management58(4), 102563 (2021)

    2021

  131. [147]

    Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent Neural Network Regularization (sep 2014),http: //arxiv.org/abs/1409.2329

    2014 arXiv

  132. [148]

    arXiv preprint arXiv:1508.01006 (2015)

    Zhang, D., Wang, D.: Relation classification via recurrent neural network. arXiv preprint arXiv:1508.01006 (2015)

    2015 arXiv

  133. [149]

    In: Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tur, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y

    Zhang, K., Yao, Y., Xie, R., Han, X., Liu, Z., Lin, F., Lin, L., Sun, M.: Open hierarchi- cal relation extraction. In: Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tur, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y. (eds.) Proceedings of the 2...

    2021

  134. [150]

    In: Proceedings of the AAAI conference on artificial intelligence

    Zhang, Q., Fu, J., Liu, X., Huang, X.: Adaptive co-attention network for named entity recognition in tweets. In: Proceedings of the AAAI conference on artificial intelligence. vol. 32 (2018)

    2018

  135. [151]

    In: Rogers, A., Boyd-Graber, J., Okazaki, N

    Zhang, R., Li, Y., Zou, L.: A novel table-to-graph generation approach for document-level joint entity and relation extraction. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1...

    2023 doi

  136. [152]

    In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t

    Zhang, Y., Bo, Z., Wang, R., Cao, J., Li, C., Bao, Z.: Entity relation extraction as dependency parsing in visually rich documents. In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t. (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing...

    2021 doi

  137. [153]

    In: Palmer, M., Hwa, R., Riedel, S

    Zhang, Y., Zhong, V., Chen, D., Angeli, G., Manning, C.D.: Position-aware attention and super- vised data improve slot filling. In: Palmer, M., Hwa, R., Riedel, S. (eds.) Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. pp. 35–45. Associa...

    2017 doi

  138. [154]

    In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t

    Zhao, J., Gui, T., Zhang, Q., Zhou, Y.: A relation-oriented clustering method for open re- lation extraction. In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t. (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. pp. 9707–

    2021

  139. [155]

    In: Rogers, A., Boyd-Graber, J., Okazaki, N

    Zhao, J., Zhan, W., Zhao, X., Zhang, Q., Gui, T., Wei, Z., Wang, J., Peng, M., Sun, M.: RE- matching: A fine-grained semantic matching method for zero-shot relation extraction. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the As...

    2023 doi

  140. [156]

    In: Rogers, A., Boyd-Graber, J., Okazaki, N

    Zhao, J., Zhang, Y., Zhang, Q., Gui, T., Wei, Z., Peng, M., Sun, M.: Actively supervised clus- tering for open relation extraction. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceed- ings of the 61st Annual Meeting of the Association for Computational Linguistics (Vo...

    2023 doi

  141. [157]

    In: Rogers, A., Boyd-Graber, J., Okazaki, N

    Zhao, J., Zhao, X., Zhan, W., Zhang, Q., Gui, T., Wei, Z., Chen, Y.W., Gao, X., Huang, X.: Open set relation extraction via unknown-aware training. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational L...

    2023 doi

  142. [158]

    In: Rogers, A., Boyd-Graber, J., Okazaki, N

    Zhao, W., Cui, Y., Hu, W.: Improving continual relation extraction by distinguishing analogous se- mantics. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. ...

    2023 doi

  143. [159]

    In: Rogers, A., Boyd-Graber, J., Okazaki, N

    Zheng, C., Feng, J., Cai, Y., Wei, X., Li, Q.: Rethinking multimodal entity and relation extrac- tion from a translation point of view. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Pro- ceedings of the 61st Annual Meeting of the Association for Computational Linguistics...

    2023 doi

  144. [160]

    In: Proceedings of the 29th ACM International Conference on Multimedia

    Zheng, C., Feng, J., Fu, Z., Cai, Y., Li, Q., Wang, T.: Multimodal relation extraction with efficient graph alignment. In: Proceedings of the 29th ACM International Conference on Multimedia. pp. 5298– 5306 (2021)

    2021

  145. [161]

    In: Rogers, A., Boyd-Graber, J., Okazaki, N

    Zheng, Y., Hao, A., Luu, A.T.: Jointprop: Joint semi-supervised learning for entity and relation extrac- tion with heterogeneous graph-based propagation. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computati...

    2023 doi

  146. [162]

    (eds.) Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

    Zhong,Z.,Chen,D.:Afrustratinglyeasyapproachforentityandrelationextraction.In:Toutanova,K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tur, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y. (eds.) Proceedings of the 2021 Conference of the North American Chapte...

    2021 doi

  147. [163]

    In: He, Y., Ji, H., Li, S., Liu, Y., Chang, C.H

    Zhou, W., Chen, M.: An improved baseline for sentence-level relation extraction. In: He, Y., Ji, H., Li, S., Liu, Y., Chang, C.H. (eds.) Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Jo...

    2022

  148. [164]

    In: Proceedings of the AAAI conference on artificial intelligence

    Zhou, W., Huang, K., Ma, T., Huang, J.: Document-level relation extraction with adaptive threshold- ing and localized context pooling. In: Proceedings of the AAAI conference on artificial intelligence. vol. 35, pp. 14612–14620 (2021)

    2021

  149. [165]

    In: Rogers, A., Boyd-Graber, J., Okazaki, N

    Zhou, W., Zhang, S., Naumann, T., Chen, M., Poon, H.: Continual contrastive finetuning improves low-resource relation extraction. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume...

    2023 doi

  150. [166]

    In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV)

    Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., Fidler, S.: Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV)....

    2015 doi

  151. [9672]

    https://doi.org/10.18653/v1/2021.emnlp-main.761, https://aclanthology.org/2021

    Association for Computational Linguistics, Online and Punta Cana, Dominican Republic (Nov 2021). https://doi.org/10.18653/v1/2021.emnlp-main.761, https://aclanthology.org/2021. emnlp-main.761

    2021 doi

  152. [9718]

    https://doi.org/10.18653/v1/2021.emnlp-main.765, https://aclanthology.org/2021

    Association for Computational Linguistics, Online and Punta Cana, Dominican Republic (Nov 2021). https://doi.org/10.18653/v1/2021.emnlp-main.765, https://aclanthology.org/2021. emnlp-main.765

    2021 doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.