Extracting Cause-Effect Pairs from a Sentence with a Dependency-Aware Transformer Model

Abrar Jahin; Md Ahsanul Kabir; Mohammad Al Hasan

REVIEW 5 major objections 4 minor 45 references

Extracting Cause-Effect Pairs from a Sentence with a Dependency-Aware Transformer Model

T0 review · 5 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A gated two-tower transformer that adds dependency-tree attention beats BERT at cause-effect extraction.

desk verdict DepBERT is a plausible architecture but the evaluation has a load-bearing split-contamination problem and an unvalidated LLM-generated dataset, so the headline result does not yet hold. read the letter →

arxiv 2507.09925 v1 pith:PHTM2GWL submitted 2025-07-14 cs.LG

Md Ahsanul Kabir , Abrar Jahin , Mohammad Al Hasan This is my paper · ORCID

classification cs.LG

keywords cause-effectextractiondependencytreetransformertokenclassificationBERTgatedattentionPOStagscausality

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

The paper sets out to show that a transformer can extract cause and effect phrases more accurately when its attention is made aware of the sentence's dependency tree. To that end, the authors build DepBERT, a two-tower model whose second tower restricts attention to neighboring tokens in the spaCy dependency graph and whose gate fuses that syntactic signal with ordinary BERT contextual embeddings. They evaluate the model as a token classifier on the SemEval and SCITE benchmarks plus a new 22,273-sentence medical dataset generated by an LLM, reporting higher exact-match accuracy and F1 than BERT, Sentence-BERT, LLaMA, and LSTM-based baselines on all three. The significance, if the results hold, is that grammatical structure can be injected into a pretrained transformer through an additional attention channel rather than through retraining or prompting.

What carries the argument

The load-bearing machinery is the gated two-tower encoder. The left tower is the standard BERT encoder; the right tower is a graph-attention encoder over the dependency tree, where an affinity score between connected tokens is $a_{ik} = (v_i W_1)(v_k W_2)^T$, attention weights are softmax over the neighbor set, and the output is a weighted sum of value vectors passed through a residual normalization and a GELU feed-forward layer. POS-tag embeddings are added to the token, positional, and input embeddings in both towers. The outputs of the two towers, $\mathbf{e}_i^b$ and $\mathbf{e}_i^t$, are fused by $\mathbf{e}_i^s = \sigma(\mathbf{e}_i^b W_6 + c)$ and $\mathbf{e}_i = \mathbf{e}_i^s \odot \mathbf{e}_i^b + (1-\mathbf{e}_i^s) \odot \mathbf{e}_i^t$, which is a learned per-token gate. This gate lets the model decide how much of the final token representation comes from the dependency structure versus the sequential context.

What would settle it

Rewire the dependency edges in the right tower randomly while preserving each token's degree, retrain, and check whether exact-match accuracy falls substantially; if it does not, the specific dependency structure is not carrying the reported gain. A second check is to have human annotators verify a random sample of CAUSAL-GPT sentences and rerun the comparison on that verified subset: if DepBERT's margin over BERT largely disappears, the headline numbers are an artifact of the generated data's repetitive templates.

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Extended reading notes

Core claim

The paper's central claim is that incorporating a sentence's dependency tree into a transformer's attention mechanism improves extraction of cause-effect pairs. DepBERT runs two encoders in parallel: a standard BERT tower that computes full self-attention over all tokens, and a dependency tower in which each token's attention is restricted to its neighbors in the dependency graph, with query, key, and value matrices and a softmax over that neighbor set. Each token's input is the sum of its input, positional, and part-of-speech embeddings. The two towers' outputs are merged with a learned gate, so the final representation can weigh sequential co-occurrence evidence against syntactic evidence per token. Trained on token-level labels for cause, effect, special, and other tokens, the model is reported to outperform all compared baselines in exact-match accuracy and F1 across SemEval, SCITE, and CAUSAL-GPT.

Load-bearing premise

The evaluation's validity assumes the spaCy dependency parses and the automatically generated CAUSAL-GPT cause/effect labels are accurate enough that the measured improvements reflect genuine causality extraction rather than artifacts of the parser or the text generator.

Editorial extensions

If this is right

If DepBERT's reported gains hold, dependency-edge attention is a viable way to inject syntax into transformer token classification without changing the pretraining objective.
On all three datasets, the exact-match accuracy of DepBERT is the highest among the compared models, so the dependency channel plus POS tags yields a practical accuracy increase in cause-effect phrase extraction.
The CAUSAL-GPT dataset of 22,273 sentences gives the field a large training resource, though its automatic generation means labels should be inspected before reuse.
Because the two ablation baselines (BERT plus dependency, BERT plus POS tags) both trail the full model, the paper concludes that combining dependency structure and POS information contributes more than either alone.

Reading between the lines

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

We infer that the gated fusion could generalize to other relation-extraction tasks, such as drug-drug interaction or protein-protein interaction, where dependency edges carry much of the semantic signal; the paper does not test these tasks.
We infer that the model's accuracy will depend on parser quality; a testable extension would replace spaCy with gold parses or another parser to measure how much of the gain is parser-driven.
We infer that the CAUSAL-GPT template structure could inflate the absolute numbers: if the LLM repeats a narrow set of sentence frames, a model can memorize surface patterns, so human-validated diversity checks would make the benchmark's numbers more interpretable.
We infer that the gate's per-token values could be used diagnostically: tokens where the gate favors the dependency tower may correspond to the causal verb and its arguments, which could be analyzed in a follow-up study.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The paper to know about: DepBERT, a two-tower transformer that injects dependency-tree attention and POS tags into a BERT-like encoder for cause-effect phrase extraction. The exact gated combination is new, and the authors deserve credit for a readable model description, including ablations that separate dependency and POS contributions. The CAUSAL-GPT dataset is a real artifact, though currently unreleased.

What the paper does well: the model design is clear, the gating mechanism is explained, and the ablations show both dependency and POS tags matter. The writing is clean. If the evaluation were sound, the gains over BERT would be a useful increment for a contained NLP task.

Where it falls apart: three issues, roughly in severity. First, the SemEval evaluation is contaminated. They merge the original train and test partitions and then randomly re-split 60/30/10. Original test instances can appear in training, so the SemEval numbers in Table 3 are not interpretable. That is a real protocol violation, not a quibble. Second, the CAUSAL-GPT dataset is acknowledged to contain duplicated, templated sentences, and the paper does not state that deduplication or near-duplicate removal was done before the random split. With examples like "X can lead to Y", a token tagger can ride lexical cues. Without a leakage analysis or human validation, the 85.8% exact-match accuracy on that dataset cannot support the headline. Third, they do not compare against the syntax-augmented transformers they cite (Ahmad et al. 2021, Sachan et al. 2020), which are the closest existing approaches; that omission makes "state-of-the-art" hard to assess. Also, DepBERT has roughly double BERT's parameter count, and there are no error bars or multiple seeds, so it is unclear whether the gains come from syntax or capacity.

None of this requires the architecture to be wrong. The idea is plausible, and the ablation pattern is consistent across datasets. But as written, the central claim is not yet supported.

This paper is for people working on syntax-aware transformers for relation extraction, and for anyone who cares about evaluation hygiene in NLP. It deserves a serious referee, but only with a clear expectation of heavy revision: fix the SemEval split, release CAUSAL-GPT with a deduplication statement, add the missing syntax-aware baselines, and report variance. I would engage with it after those changes, not before.

Recommendation: send to peer review for a revise-and-resubmit judgement, but the current version should not be accepted as-is.

Referee Report

5 major / 4 minor

Summary. The paper proposes DepBERT, a two-tower transformer model that combines a standard BERT encoder with a dependency-tree-aware attention tower, gated together to produce token representations for cause/effect phrase extraction. The authors introduce a new LLM-generated dataset, CAUSAL-GPT, with 22,273 instances, and report experiments on SemEval, SCITE, and CAUSAL-GPT showing that DepBERT outperforms baselines including BERT, LLaMA, and LSTM-based methods. The core claim is that integrating dependency structures and POS tags into the transformer architecture improves exact-match accuracy for causal phrase extraction.

Significance. If the reported gains are valid, the dependency-aware gated architecture is a plausible contribution to supervised causality extraction, and the new dataset could be a resource for the community. The paper includes useful ablations (BERT plus dependency, BERT plus POS tags) and consistent qualitative trends across three datasets, which lend initial plausibility to the mechanism. However, the evaluation protocol contains serious flaws—especially the merging of predefined train/test splits in SemEval and the acknowledged duplication in CAUSAL-GPT—that currently prevent the results from supporting the central claim.

major comments (5)

[§4.1, SemEval data] The manuscript states that for SemEval, "Train, test and validation partitions are then merged to concatenate into a single dataset," after which a new random 60/30/10 split is created. This merges the original test partition into the training pool, so original test instances can appear in the new training and validation subsets. Consequently, the numbers in Table 3 cannot be interpreted as a valid measure of generalization, and the claim that DepBERT outperforms baselines on SemEval is not supported. The authors must use the original predefined splits or provide a strong justification for why incorporating the original test set into training is acceptable.
[§4.1, CAUSAL-GPT dataset] The paper acknowledges that "the generated sentences may exhibit duplication" and that preprocessing is needed, but it does not state whether duplicates or near-duplicates were removed before the random 60/30/10 split. Given the templated nature of the examples in Table 1 (e.g., "Diabetes can lead to blindness if left uncontrolled", "Smoking is a major risk factor for developing lung cancer"), lexical overlap between train and test can inflate exact-match accuracy through memorization. The authors must report a deduplication analysis (e.g., exact and near-duplicate rates between train and test) and either remove duplicates or use a split that prevents leakage.
[§4.3, Experimental setup] All results are reported from "the initial stable runs" with no multiple seeds or variance. Fine-tuning of transformer models is stochastic, and the margins in Table 3 are very small (e.g., DepBERT 0.842 vs. BERT plus dependency 0.841 exact accuracy). Without standard deviations or significance tests, the claim that DepBERT is "better" than baselines is not statistically supported. The authors should run multiple seeds and report mean and standard deviation, or at least show that the differences are consistent across seeds.
[§4.3, Model parameters] DepBERT has 227 million trainable parameters, while BERT has 109 million and the BERT-plus-dependency baseline is reported as 110 million. The comparison is therefore confounded by parameter count: the improvement attributed to the dependency-aware mechanism could simply reflect additional model capacity. The authors should ablate with a matched-capacity baseline (e.g., a larger BERT variant or a two-tower model without dependency edges) to isolate the contribution of the dependency structure.
[§2 and §4.2, Missing comparisons] The paper cites syntax-augmented transformers (Ahmad et al., 2021; Sachan et al., 2020) as related work but does not compare against them in the experiments. Since these are the closest state-of-the-art competitors for syntax-aware models, the claim of outperforming "various state-of-the-art supervised causality extraction methods" is not fully established. The authors should add such comparisons or explain why they are not applicable.

minor comments (4)

[§4.3, Parameter count list] The sentence "for LLaMA, BERT, BERT plus Dependency, BERT plus POS tags, Sentence-BERT, the number of trainable parameters stands at 524 million, 109 million, 110 million, and 110 million, respectively" lists five methods but only four parameter counts, and the LLaMA value (524M) is inconsistent with the earlier statement that LLaMA has 7B-65B parameters. This should be clarified.
[§3.3, Notation] In the attention equations, the symbol V is used both for the vertex set of the dependency graph and for the set of tokens connected to a given token via edges, which is confusing. Also, the softmax in the attention equation is taken over the set V but the denominator is written as a sum over j in [1,|V|] without explicitly defining the correspondence; please align the notation.
[§4.2.4, BERT plus dependency baseline] The description of "BERT plus dependency" says it differs from BERT in that it is "partially pretrained" but does not specify whether it uses the same two-tower architecture as DepBERT. Clarify whether this baseline includes the dependency-attention tower without POS tags, and why its parameter count is only 110M when DepBERT has 227M.
[§4.4, Exact-match accuracy] The definition of exact accuracy states that a model must predict all causal and effect tokens correctly, but it is not stated whether a sentence with multiple cause-effect pairs is scored per pair or per sentence. Please clarify the exact scoring unit.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: DepBERT's gains are measured on held-out labels with independent baselines; self-citations are motivational only.

full rationale

Walking the derivation chain, DepBERT's central claim is an empirical model comparison, not a theorem derived from its inputs. The model is trained to minimize cross-entropy on token labels and is evaluated with exact-match accuracy on held-out splits of SemEval, SCITE, and CAUSAL-GPT. Dependency trees and POS tags are additional input features, not predictions being fitted; the gating and attention equations define an architecture and do not encode the answer. The paper's own ablations (BERT plus dependency, BERT plus POS tags) make the contribution of the dependency channel an empirical finding. The CAUSAL-GPT corpus is self-created, and the paper admits it "may exhibit duplication" (Section 4.1), which is a data-validity and potential leakage concern, not derivation-level circularity: no parameter is fitted to test labels, and the same superiority pattern appears on the externally sourced SemEval and SCITE datasets. Self-citations to Kabir et al. (2021, 2022) motivate the use of dependency structure, but those citations are not the proof of DepBERT's performance; the experiments are held-out and self-contained. No uniqueness theorem, imported ansatz, renamed known result, or definitional equivalence appears. Therefore, no circular step is identified, and the score is 0.

Assumptions & free parameters 4 free parameters · 5 assumptions · 1 invented entities

The central claim does not rest on a formal derivation; it depends on empirical pipeline choices. The main loaded assumptions are the correctness of automatic dependency parses and the validity of the LLM-generated corpus, plus standard pretrained-model and training assumptions.

free parameters (4)

learning rate = 0.001
Set to a default value for all models; no tuning or sensitivity analysis is reported.
batch size = 128
Fixed for all models without reported tuning.
early stopping patience = 10
Used with a maximum of 1000 epochs; stopping rule is a modeling choice that affects final weights.
right-tower weight matrices W1-W5 and gate parameters W6, c, gamma, beta = learned from data, not reported
These trainable parameters define the dependency attention, feed-forward layers, and gating fusion; no values or initialization details are given.

assumptions (5)

domain assumption Pretrained bert-base-uncased provides a sufficient token representation for the left tower.
The model relies on BERT's pretrained weights for the standard attention channel; no task-specific pretraining is done.
domain assumption Spacy's dependency parse of each sentence is correct and useful.
Dependency graph edges and POS tags are taken from Spacy as ground truth; parsing errors propagate into attention and labels.
ad hoc to paper LLM-generated CAUSAL-GPT sentences and labels are valid training and evaluation data.
The largest dataset is generated by an unspecified LLM; the paper says it may contain duplicates and special Unicode but provides no human validation of cause-effect annotations.
domain assumption Token-level exact matching is the appropriate evaluation metric.
Accuracy is computed only when all cause and effect tokens are predicted exactly; partial credit is ignored, a choice not justified in the paper.
standard math Standard backpropagation and cross-entropy training behaves as expected.
The paper uses multinomial cross-entropy loss and Adam without formal justification; this is a standard machine learning assumption.

invented entities (1)

CAUSAL-GPT dataset
purpose: Large LLM-generated corpus for training and evaluating cause-effect extraction.
The corpus is generated by an unspecified LLM, not released, and lacks human validation. Results on it may reflect the generator's templates rather than general language behavior.

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Cite this review

Pith. "Pith review of Extracting Cause-Effect Pairs from a Sentence with a Dependency-Aware Transformer Model." pith.science (2026). https://pith.science/paper/PHTM2GWL

@misc{pith2026250709925,
  author       = {Pith},
  title        = {Pith review of: Extracting Cause-Effect Pairs from a Sentence with a Dependency-Aware Transformer Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PHTM2GWL}},
  note         = {Machine review of arXiv:2507.09925}
}

read the original abstract

Extracting cause and effect phrases from a sentence is an important NLP task, with numerous applications in various domains, including legal, medical, education, and scientific research. There are many unsupervised and supervised methods proposed for solving this task. Among these, unsupervised methods utilize various linguistic tools, including syntactic patterns, dependency tree, dependency relations, etc. among different sentential units for extracting the cause and effect phrases. On the other hand, the contemporary supervised methods use various deep learning based mask language models equipped with a token classification layer for extracting cause and effect phrases. Linguistic tools, specifically, dependency tree, which organizes a sentence into different semantic units have been shown to be very effective for extracting semantic pairs from a sentence, but existing supervised methods do not have any provision for utilizing such tools within their model framework. In this work, we propose DepBERT, which extends a transformer-based model by incorporating dependency tree of a sentence within the model framework. Extensive experiments over three datasets show that DepBERT is better than various state-of-the art supervised causality extraction methods.

Figures

Figures reproduced from arXiv: 2507.09925 by the authors.

**Figure 2.** DEPBERT Model Architecture for Token Classification kens in second channel holds POS information, these POS tags are utilized in DEPBERT, allowing it to incorporate semantic information of the tokens into the model. Last, we combine the two representations of token embedding coming from two channels: traditional token-based, and dependency graph based, which is then passed to a layer for token classification. In t… view at source ↗

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Works this paper leans on

45 extracted references · 36 canonical work pages

[1]

URL: " 'urlintro :=

ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
[3]

Wasi Ahmad, Haoran Li, Kai-Wei Chang, and Yashar Mehdad. 2021. https://doi.org/10.18653/v1/2021.acl-long.350 Syntax-augmented multilingual BERT for cross-lingual transfer . In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Pa...

work page doi:10.18653/v1/2021.acl-long.350 2021
[4]

Alan Akbik, Duncan Blythe, and Roland Vollgraf. 2018. https://aclanthology.org/C18-1139 Contextual string embeddings for sequence labeling . In Proceedings of the 27th International Conference on Computational Linguistics, pages 1638--1649, Santa Fe, New Mexico, USA. Association for Computational Linguistics

work page 2018
[5]

Ning An, Yongbo Xiao, Jing Yuan, Yang Jiaoyun, and Gil Alterovitz. 2019 a . https://doi.org/10.1016/j.compbiomed.2019.103524 Extracting causal relations from the literature with word vector mapping . volume 115, page 103524

work page arXiv 2019
[6]

Ning An, Yongbo Xiao, Jing Yuan, Jiaoyun Yang, and Gil Alterovitz. 2019 b . Extracting causal relations from the literature with word vector mapping. Computers in biology and medicine, 115:103524

work page 2019
[7]

John Atkinson and Alejandro Rivas. 2008. Discovering novel causal patterns from biomedical natural-language texts using bayesian nets. IEEE Transactions on Information technology in Biomedicine, 12(6):714--722

work page 2008
[8]

Tal Azagi, Hein Sprong, Dieuwertje Hoornstra, and Joppe Hovius. 2020. https://doi.org/10.3390/pathogens9020150 Evaluation of disease causality of rare ixodes ricinus-borne infections in europe . volume 9, page 150

work page doi:10.3390/pathogens9020150 2020

Show all 45 references

[9]

Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016. Enriching word vectors with subword information. volume 5

2016
[10]

Jason Brownlee. 2017. Why one-hot encode data in machine learning. Machine Learning Mastery, pages 1--46

2017
[11]

Du-Seong Chang and Key-Sun Choi. 2006. https://doi.org/10.1016/j.ipm.2005.04.004 Incremental cue phrase learning and bootstrapping method for causality extraction using cue phrase and word pair probabilities . volume 42, pages 662--678

2006 doi
[12]

Terapat Chansai, Ruksit Rojpaisarnkit, Teerakarn Boriboonsub, Suppawong Tuarob, Myat Su Yin, Peter Haddawy, Saeed-Ul Hassan, and Mihai Pomarlan. 2021. Automatic cause-effect relation extraction from dental textbooks using bert. In Towards Open and Trustworthy Digital Societies...

2021
[13]

Tirthankar Dasgupta, Rupsa Saha, Lipika Dey, and Abir Naskar. 2018. https://doi.org/10.18653/v1/W18-5035 Automatic extraction of causal relations from text using linguistically informed deep neural networks . In Proceedings of the 19th Annual SIG dial Meeting on Discourse and ...

2018 doi
[14]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding

2018
[15]

Quang Do, Yee Seng Chan, and Dan Roth. 2011. Minimally supervised event causality identification. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 294--303

2011
[16]

Conditional Random Fields. 2001. Probabilistic models for segmenting and labeling sequence data. In ICML 2001

2001
[17]

Roxana Girju, Preslav Nakov, Vivi Nastase, Stan Szpakowicz, Peter Turney, and Deniz Yuret. 2007. https://aclanthology.org/S07-1003 S em E val-2007 task 04: Classification of semantic relations between nominals . In Proceedings of the Fourth International Workshop on Semantic E...

2007
[18]

David Harris and Sarah L Harris. 2012. Digital design and computer architecture. Morgan Kaufmann

2012
[19]

Iris Hendrickx, Su Nam Kim, Zornitsa Kozareva, Preslav Nakov, Diarmuid \'O S \'e aghdha, Sebastian Pad \'o , Marco Pennacchiotti, Lorenza Romano, and Stan Szpakowicz. 2010. https://aclanthology.org/S10-1006 S em E val-2010 task 8: Multi-way classification of semantic relations...

2010
[20]

Dan Hendrycks and Kevin Gimpel. 2016. http://arxiv.org/abs/1606.08415 Bridging nonlinearities and stochastic regularizers with gaussian error linear units . CoRR, abs/1606.08415

2016 arXiv
[21]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation, 9(8):1735--1780

1997
[22]

Matthew Honnibal and Ines Montani. 2020. spaCy 2.2.3 : Industrial-strength natural language processing. In https://spacy.io/

2020
[23]

Md Kabir, AlJohara Almulhim, Xiao Luo, and Mohammad Hasan. 2022. https://doi.org/10.1007/s41666-022-00116-z Informative causality extraction from medical literature via dependency-tree based patterns . volume 6, pages 295--316

2022 doi
[24]

Ahsanul Kabir, Typer Phillips, Xiao Luo, and Mohammad Al Hasan

Md. Ahsanul Kabir, Typer Phillips, Xiao Luo, and Mohammad Al Hasan. 2021. http://arxiv.org/abs/2104.01523 Asper: Attention-based approach to extract syntactic patterns denoting semantic relations in sentential context

2021 arXiv
[25]

Khoo, Jaklin Kornfilt, R

C. Khoo, Jaklin Kornfilt, R. Oddy, and Sung-Hyon Myaeng. 1998. https://doi.org/10.1093/llc/13.4.177 Automatic extraction of cause-effect information from newspaper text without knowledge-based inferencing . volume 13, pages 177--186

1998 doi
[26]

Christopher Khoo, Sung-Hyon Myaeng, and Robert Oddy. 2001. https://doi.org/10.1016/S0306-4573(00)00022-4 Using cause-effect relations in text to improve information retrieval precision . volume 37, pages 119--145

2001 doi
[27]

Dong-gi Lee and Hyunjung Shin. 2017. Disease causality extraction based on lexical semantics and document-clause frequency from biomedical literature. BMC medical informatics and decision making, 17(1):53

2017
[28]

Zhaoning Li, Qi Li, Xiaotian Zou, and Jiangtao Ren. 2019. Causality extraction based on self-attentive bilstm-crf with transferred embeddings

2019
[29]

Zhaoning Li, Qi Li, Xiaotian Zou, and Jiangtao Ren. 2021. Causality extraction based on self-attentive bilstm-crf with transferred embeddings. Neurocomputing, 423:207--219

2021
[30]

Tomas Mikolov, G.s Corrado, Kai Chen, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. pages 1--12

2013
[31]

Thien Huu Nguyen and Ralph Grishman. 2015. Relation extraction: Perspective from convolutional neural networks. In Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing, pages 39--48

2015
[32]

Nanyun Peng, Hoifung Poon, Chris Quirk, Kristina Toutanova, and Wen-tau Yih. 2017. Cross-sentence n-ary relation extraction with graph lstms. Transactions of the Association for Computational Linguistics, 5:101--115

2017
[33]

Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, and Christopher D. Manning. 2020. Stanza: A Python natural language processing toolkit for many human languages. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

2020
[34]

Alessandro Raganato and J \"o rg Tiedemann. 2018. An analysis of encoder representations in transformer-based machine translation. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. The Association for Computational Linguistics

2018
[35]

Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084

2019 arXiv
[36]

Devendra Singh Sachan, Yuhao Zhang, Peng Qi, and William Hamilton. 2020. Do syntax trees help pre-trained transformers extract information? arXiv preprint arXiv:2008.09084

2020 arXiv
[37]

Zixing Song and Irwin King. 2022. Hierarchical heterogeneous graph attention network for syntax-aware summarization. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 11340--11348

2022
[38]

Vettigli, and Francesco Mele

Antonio Sorgente, G. Vettigli, and Francesco Mele. 2013 a . Automatic extraction of cause-effect relations in natural language text. volume 1109, pages 37--48

2013
[39]

Antonio Sorgente, Giuseppe Vettigli, and Francesco Mele. 2013 b . Automatic extraction of cause-effect relations in natural language text. DART@ AI* IA, 2013:37--48

2013
[40]

Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth \'e e Lacroix, Baptiste Rozi \`e re, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971

2023 arXiv
[41]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, 30

2017
[42]

Petar Veli c kovi \'c , Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903

2017 arXiv
[43]

David Wald, Malcolm Law, and Joan Morris. 2002. https://doi.org/10.1136/bmj.325.7374.1202 Homocysteine and cardiovascular disease: Evidence on causality from a meta-analysis . BMJ (Clinical research ed.), 325:1202

2002
[44]

Yaxuan Wang, Hanqing Lu, Yunwen Xu, Rahul Goutam, Yiwei Song, and Bing Yin. 2021. https://www.amazon.science/publications/queen-neural-query-rewriting-in-e-commerce Queen: Neural query rewriting in e-commerce . In The Web Conference 2021

2021
[45]

Sendong Zhao, Meng Jiang, Ming Liu, Bing Qin, and Ting Liu. 2018. Causaltriad: toward pseudo causal relation discovery and hypotheses generation from medical text data. In Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health...

2018

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[1] [1]

URL: " 'urlintro :=

ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

Wasi Ahmad, Haoran Li, Kai-Wei Chang, and Yashar Mehdad. 2021. https://doi.org/10.18653/v1/2021.acl-long.350 Syntax-augmented multilingual BERT for cross-lingual transfer . In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Pa...

work page doi:10.18653/v1/2021.acl-long.350 2021

[4] [4]

Alan Akbik, Duncan Blythe, and Roland Vollgraf. 2018. https://aclanthology.org/C18-1139 Contextual string embeddings for sequence labeling . In Proceedings of the 27th International Conference on Computational Linguistics, pages 1638--1649, Santa Fe, New Mexico, USA. Association for Computational Linguistics

work page 2018

[5] [5]

Ning An, Yongbo Xiao, Jing Yuan, Yang Jiaoyun, and Gil Alterovitz. 2019 a . https://doi.org/10.1016/j.compbiomed.2019.103524 Extracting causal relations from the literature with word vector mapping . volume 115, page 103524

work page arXiv 2019

[6] [6]

Ning An, Yongbo Xiao, Jing Yuan, Jiaoyun Yang, and Gil Alterovitz. 2019 b . Extracting causal relations from the literature with word vector mapping. Computers in biology and medicine, 115:103524

work page 2019

[7] [7]

John Atkinson and Alejandro Rivas. 2008. Discovering novel causal patterns from biomedical natural-language texts using bayesian nets. IEEE Transactions on Information technology in Biomedicine, 12(6):714--722

work page 2008

[8] [8]

Tal Azagi, Hein Sprong, Dieuwertje Hoornstra, and Joppe Hovius. 2020. https://doi.org/10.3390/pathogens9020150 Evaluation of disease causality of rare ixodes ricinus-borne infections in europe . volume 9, page 150

work page doi:10.3390/pathogens9020150 2020