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REVIEW 4 major objections 4 minor 43 references

Local Embeddings for Relational Data Integration

T0 review · 4 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Local embeddings learned from a tripartite graph of values, rows, and columns outperform pre-trained text embeddings for data integration.

desk verdict Useful systems paper whose tripartite graph with RID/CID nodes is a real contribution; the universal 'beats pre-trained' claim is overstated but the qualified result holds up. read the letter →

arxiv 1909.01120 v2 pith:EUB5U2N7 submitted 2019-09-03 cs.DB cs.CLcs.LG

Riccardo Cappuzzo , Paolo Papotti , Saravanan Thirumuruganathan This is my paper · ORCID
classification cs.DBcs.CLcs.LG
keywords dataintegrationentityresolutionschemamatchinglocalembeddingsgraphtripartiterandomwalksrelational
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 relational databases can supply their own task-specific word vectors. It represents the combined tables as a tripartite graph with three kinds of nodes—cell values, row identifiers, and column identifiers—and generates a training corpus by running random walks over that graph, so that a single walk moves through values, tuples, and attributes. The claim is that a standard skip-gram embedding trained on these walks makes rows and columns first-class citizens of the vector space, and that the resulting local embeddings beat pre-trained text embeddings on schema matching and entity resolution in both supervised and unsupervised settings. A sympathetic reader would care because enterprise data frequently uses custom vocabulary that large-corpus models have never seen, and the paper's method needs no external corpus to produce integration signals.

What carries the argument

The load-bearing object is the tripartite graph: three node types—token nodes for cell values, RID nodes for tuples, and CID nodes for attributes—with edges from every value to its tuple and its column. Random walks over this graph are materialized as sentences, and those sentences are the corpus for a skip-gram embedding learner. The graph does the conceptual work: it makes row and column identities explicit in the vector space, allows rare values to appear often enough by giving every node a walk budget, and gives a natural place to inject external knowledge by merging nodes or replacing one token with another during sentence construction. The same structure also supports the paper's optimizations for imbalanced relations, missing values, numeric attributes, and multi-word cells.

What would settle it

Run EmbDI on two tables about the same entities with zero shared tokens—for instance, one table in English and one in French, or one using country codes and the other full country names—without external dictionaries or alignment; if the nearest-neighbor matches among RID and CID vectors do not rise well above chance, the co-occurrence-as-semantics assumption fails.

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Extended reading notes

Core claim

The central claim is that co-occurrence in random walks over a compact tripartite graph is enough to learn embeddings that carry the semantics needed for data integration. Because every cell value is connected to the row that contains it and to the column it belongs to, a walk can juxtapose values from the same tuple, values from the same attribute, or values connected only through the row and column identifiers. When those walks are used as sentences for a skip-gram model, the learned vectors put matching rows near each other and matching columns near each other, and the paper's experiments show that these vectors improve entity resolution and schema matching relative to pre-trained vectors, both when used directly with nearest-neighbor matching and when fed into supervised deep-learning matchers.

Load-bearing premise

The load-bearing premise is that co-occurrence in graph walks is a faithful proxy for the semantic similarity that data integration needs; if two values, rows, or columns never share a graph neighborhood, their embeddings will be unrelated no matter how conceptually close they are.

Editorial extensions

If this is right

  • Deep-learning matchers for entity resolution can swap pre-trained input vectors for these local vectors and gain on average about 5 absolute points of F-measure when only 5% of matches are labeled.
  • Schema matching can run without attribute labels by measuring cosine distance between column-ID vectors, and an existing embedding-based schema matcher improves by an average of about 6 absolute points when given these embeddings instead of pre-trained ones.
  • Rare and custom tokens are not discarded: the walk budget guarantees each node appears as a walk start, which directly addresses the vocabulary gap of pre-trained models.
  • Datasets with long multi-word values benefit most from the 'overlap' tokenization, which keeps values shared across datasets as single nodes and splits the rest word by word; this variant wins most entity-resolution scenarios in the paper.
  • The embedding construction is light enough to run on a laptop for a million-row table, in settings where graph baselines end in memory errors or timeout.

Reading between the lines

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

  • Because the graph makes rows, columns, and values live in one vector space, the same embeddings should supply features for single-table tasks such as duplicate detection and error detection; the paper evaluates only cross-table integration, so that use is my extrapolation.
  • A hybrid that starts from pre-trained vectors for generic terms and then refines them with local graph walks could inherit both wide lexical coverage and enterprise-specific vocabulary; the paper lists this combination as future work, not as a tested result.
  • The paper's sensitivity results suggest that the random-walk policy, not just the embedding learner, is the main tuning lever; learning transition probabilities from a handful of labeled matches could be a direct extension of the method.
  • Because the graph is built incrementally from tables, the framework could in principle be applied to streaming data by extending the graph and refreshing walks; the paper does not address incremental updates.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper proposes EmbDI, a framework for learning local embeddings of relational data for data integration tasks. It represents two relational datasets as a heterogeneous tripartite graph with token, record-id, and column-id nodes, generates a sentence corpus via uniform random walks on this graph, and trains word2vec embeddings on the resulting corpus. The method is evaluated on eight benchmark datasets for schema matching, entity resolution, and token matching, in both unsupervised and supervised settings, and compared against pre-trained fastText embeddings, graph-embedding baselines (Node2Vec, Harp), and existing integration systems (SeepP, DeepER). The central claim is that local embeddings from EmbDI outperform pre-trained embeddings for data integration on enterprise-style data with custom vocabulary.

Significance. If the empirical claims hold, the paper makes a useful contribution: it offers a practical way to obtain integration-specific embeddings without relying on pre-trained text models, which is attractive for enterprise datasets with specialized vocabularies. The tripartite graph design that treats record IDs and column IDs as first-class nodes is a reasonable and novel mechanism for capturing row- and column-level semantics. The paper includes a public code repository, an ablation study, and a diverse set of datasets, which strengthen reproducibility and the generalizability of the framework. The central 'local beats pre-trained' claim is plausible and relevant, but, as detailed below, the current evidence does not fully support the universal phrasing used in the text.

major comments (4)
  1. [Section 7.2, Table 4] The claim 'In every case, local embeddings obtained from our graph outperform pre-trained ones' is not supported by the reported data. In the unsupervised ER results, for IA fastText attains F-measure 0.10 while EmbDI-S and EmbDI-F attain 0.09; for FZ, fastText attains 0.99 while EmbDI-S attains 0.98. The claim is only defensible if restricted to the EmbDI-O variant, but the sentence as written refers to local embeddings generally. Furthermore, the tables report a single F-measure per configuration, and both random-walk generation and word2vec training are stochastic; the 0.01 margins on IA and FZ are within the range of seed-to-seed variability. The authors should report repeated runs with variance or significance tests, or explicitly and carefully qualify the claim.
  2. [Section 7.3] The experimental protocol uses a hand-picked default configuration (walk length 60, window size 3, 300 dimensions, corpus size formula (#dist.values + #rows) * 1000, n_top = 10) and then reports in the ablation that task-specific changes such as CBOW instead of skip-gram, or a walk length of 5 for SM, improve F-measure by several absolute points. This makes it unclear whether the reported advantage of EmbDI over pre-trained baselines reflects the method's intrinsic quality or favorable hyperparameter choices. The paper should provide a sensitivity analysis over a small grid of these parameters for the main integration tasks, or at least state explicitly whether the comparative conclusions in Tables 3 and 4 hold across the grid.
  3. [Section 6, Algorithm 6 and Table 5] The unsupervised ER and SM algorithms rely on mutual nearest neighbors with a fixed n_top, and Table 5 shows that F-measure varies noticeably with n_top (for example, on IA F-measure ranges from 0.068 at n_top=1 to 0.100 at n_top=100). The default n_top=10 is used without a principled justification, and for datasets such as IA the reported advantage over the fastText baseline is smaller than the variation induced by n_top. The authors should justify the choice of n_top and show that the qualitative comparison to baselines is stable for other values.
  4. [Section 7.1, Table 2] The summary statement that 'local embeddings generated by EmbDI are superior to all other methods' is an overstatement of Table 2. EmbDI is not the best method on several entries, for example BB MR (0.50 vs. Node2Vec 0.66) and AG MC (1.00, tied with Harp and Node2Vec), and some baselines could not complete large datasets due to memory or time limits. The sentence should be revised to say that EmbDI is superior on average, and the incomplete baseline results should be taken into account when interpreting the comparison.
minor comments (4)
  1. [Algorithm 5] There is an apparent typo in line 3: 'let T = C1 ∪ C 1' should presumably be 'C1 ∪ C2'.
  2. [Section 5.4, Algorithm 4] In Algorithm 4, the symbol A is used for both the anchor set (U1 ∩ U2) and the matrix of embeddings of R1, which is confusing. The variable names should be made consistent.
  3. [Section 5.3] The sentence 'Hence, the data repairing task could be used to address both missing and noisy values' is a non-sequitur in context; the preceding paragraph describes using placeholders for missing values, but does not explain how this addresses noise. The wording should be clarified.
  4. [Table 5] The column header 'nt op' should either be written as 'n_top' in text style and with a proper subscript, or spelled out; the current formatting is difficult to read.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: EmbDI is evaluated against external ground truth and its central claims are empirical, not derived from fitted inputs or self-citation.

full rationale

The paper's central claim is empirical: local embeddings from the tripartite graph random walks outperform pre-trained embeddings for schema matching and entity resolution. This is tested against hand-crafted ground truth (Section 7.2) and external baselines; no equation or derivation reduces the output to its inputs. Graph construction (Section 4.1), random-walk sentence construction (Section 4.2), and skip-gram embedding training (Section 4.3) are standard components, and the paper does not claim to derive a theorem. Hyperparameters such as walk length 60, window size 3, and the corpus-size rule of thumb are stated heuristics; the ablation in Section 7.3 reports how task-specific tuning changes results, but the headline numbers use a single declared configuration. Choosing hyperparameters is configuration selection, not a fitted parameter renamed as a prediction. The only salient self-citation (DeepER, reference [14], co-authored by Thirumuruganathan) is used as a downstream supervised ER system for comparing pre-trained versus local embeddings, not as justification of EmbDI's central premise, so the comparison remains external. Possible seed sensitivity at FZ and IA margins in Table 4 is an experimental-rigor concern about unreplicated runs, not a circularity. No load-bearing uniqueness theorem or ansatz is imported from prior work by the same authors. Under the required standard of exhibiting a specific reduction from output to input, no circular step is identifiable.

Assumptions & free parameters 5 free parameters · 3 assumptions · 0 invented entities

The paper introduces no invented physical or mathematical entities. Its central claim rests on domain assumptions about graph representation, random walks, and embedding transfer. The main free parameters are hyperparameters chosen by hand, with ablations showing sensitivity to some of them. No formal derivation is attempted.

free parameters (5)
  • Random walk length = 60
    Default walk length used in all reported experiments; ablation shows SM improves with length 5, so results are sensitive to this hand-set value.
  • Embedding dimension = 300
    Default vector dimension; ablation says reducing dimensions has limited mixed effects.
  • Context window = 3
    Word2vec window size; a larger window of 5 reduces results across all tasks.
  • Corpus size factor = 1000
    Rule of thumb: number of corpus tokens equals (number of distinct values plus number of rows) times 1000, with no theoretical justification.
  • n_top = 10
    Number of nearest neighbors kept in unsupervised entity resolution; Table 5 shows precision and recall trade-offs vary with this value.
assumptions (3)
  • domain assumption The tripartite graph with token, RID, and CID nodes captures the relational semantics relevant to data integration.
    Section 4.1 states the graph represents relationships in the relational world, but this is assumed rather than proven.
  • domain assumption Random walk co-occurrence in this graph is a valid proxy for semantic similarity between tokens, rows, and columns.
    Section 4.2 relies on the skip-gram assumption that nodes sharing neighborhoods have nearby embeddings.
  • domain assumption Word2vec skip-gram trained on walk sentences transfers to schema matching and entity resolution.
    Section 4.3 adopts word2vec as the embedding learner; this transfer is empirically evaluated, not derived.

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Cite this review

Pith. "Pith review of Local Embeddings for Relational Data Integration." pith.science (2026). https://pith.science/paper/EUB5U2N7

@misc{pith2026190901120,
  author       = {Pith},
  title        = {Pith review of: Local Embeddings for Relational Data Integration},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EUB5U2N7}},
  note         = {Machine review of arXiv:1909.01120}
}
read the original abstract

Deep learning based techniques have been recently used with promising results for data integration problems. Some methods directly use pre-trained embeddings that were trained on a large corpus such as Wikipedia. However, they may not always be an appropriate choice for enterprise datasets with custom vocabulary. Other methods adapt techniques from natural language processing to obtain embeddings for the enterprise's relational data. However, this approach blindly treats a tuple as a sentence, thus losing a large amount of contextual information present in the tuple. We propose algorithms for obtaining local embeddings that are effective for data integration tasks on relational databases. We make four major contributions. First, we describe a compact graph-based representation that allows the specification of a rich set of relationships inherent in the relational world. Second, we propose how to derive sentences from such a graph that effectively "describe" the similarity across elements (tokens, attributes, rows) in the two datasets. The embeddings are learned based on such sentences. Third, we propose effective optimization to improve the quality of the learned embeddings and the performance of integration tasks. Finally, we propose a diverse collection of criteria to evaluate relational embeddings and perform an extensive set of experiments validating them against multiple baseline methods. Our experiments show that our framework, EmbDI, produces meaningful results for data integration tasks such as schema matching and entity resolution both in supervised and unsupervised settings.

Figures

Figures reproduced from arXiv: 1909.01120 by the authors.

Figure 1
Figure 1. Illustration of a simplified vector space learned from text (prior approaches) and from data ( [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. The graph for the two tables in Figure 1. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. EmbDI ER F-measure for IM with increasing amount of missing values in the data [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗

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Works this paper leans on

43 extracted references · 37 canonical work pages

  1. [1]

    Ilyas, Mourad Ouzzani, Paolo Papotti, Michael Stonebraker, and Nan Tang

    Ziawasch Abedjan, Xu Chu, Dong Deng, Raul Castro Fernandez, Ihab F. Ilyas, Mourad Ouzzani, Paolo Papotti, Michael Stonebraker, and Nan Tang. 2016. Detecting Data Errors: Where are we and what needs to be done? PVLDB 9, 12 (2016), 993–1004

    work page 2016

  2. [2]

    Arocena, Boris Glavic, Giansalvatore Mecca, Renée J

    Patricia C. Arocena, Boris Glavic, Giansalvatore Mecca, Renée J. Miller, Paolo Papotti, and Donatello Santoro. 2015. Messing Up with BART: Error Generation for Evaluating Data-Cleaning Algorithms. PVLDB 9, 2 (2015), 36–47

    work page 2015

  3. [4]

    Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov

    work page

  4. [5]

    Rajesh Bordawekar, Bortik Bandyopadhyay, and Oded Shmueli. 2017. Cognitive database: A step towards endowing relational databases with artificial intelligence capabilities. arXiv preprint arXiv:1712.07199 (2017)

    work page Pith review arXiv 2017

  5. [6]

    Rajesh Bordawekar and Oded Shmueli. 2017. Using word embedding to enable semantic queries in relational databases. In DEEM Workshop. ACM, 5

    work page 2017

  6. [7]

    Rajesh Bordawekar and Oded Shmueli. 2019. Exploiting Latent Infor- mation in Relational Databases via Word Embedding and Application to Degrees of Disclosure.. In CIDR

    work page 2019

  7. [8]

    Öykü Özlem Çakal, Mohammad Mahdavi, and Ziawasch Abedjan. 2019. CLRL: Feature Engineering for Cross-Language Record Linkage. In EDBT. 678–681

    work page 2019

  8. [9]

    Haochen Chen, Bryan Perozzi, Yifan Hu, and Steven Skiena. 2017. HARP: Hierarchical Representation Learning for Networks. CoRR abs/1706.07845 (2017). arXiv:1706.07845 http://arxiv.org/abs/1706. 07845

    work page Pith review arXiv 2017

Show all 43 references
  1. [10]

    Xu Chu and Ihab F. Ilyas. 2019. Data Cleaning. ACM

    2019

  2. [11]

    Alexis Conneau, Guillaume Lample, Marc’Aurelio Ranzato, Ludovic Denoyer, and Hervé Jégou. 2017. Word translation without parallel data. arXiv preprint arXiv:1710.04087 (2017)

    2017 arXiv

  3. [12]

    C., AnHai Doan, Jeffrey F

    Sanjib Das, Paul Suganthan G. C., AnHai Doan, Jeffrey F. Naughton, Ganesh Krishnan, Rohit Deep, Esteban Arcaute, Vijay Raghavendra, and Youngchoon Park. 2017. Falcon: Scaling Up Hands-Off Crowd- sourced Entity Matching to Build Cloud Services. In SIGMOD

    2017

  4. [13]

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova

  5. [14]

    Muhammad Ebraheem, Saravanan Thirumuruganathan, Shafiq Joty, Mourad Ouzzani, and Nan Tang. 2018. Distributed representations of tuples for entity resolution. PVLDB 11, 11 (2018), 1454–1467

    2018

  6. [15]

    Raul Castro Fernandez and Samuel Madden. 2019. Termite: a system for tunneling through heterogeneous data. arXiv preprint arXiv:1903.05008 (2019)

    2019 arXiv

  7. [16]

    Raul Castro Fernandez, Essam Mansour, Abdulhakim A Qahtan, Ahmed Elmagarmid, Ihab Ilyas, Samuel Madden, Mourad Ouzzani, Michael Stonebraker, and Nan Tang. 2018. Seeping semantics: Linking datasets using word embeddings for data discovery. In ICDE

    2018

  8. [17]

    FigureEight. 2016. Data Science Report. https://visit.figure-eight.com/ data-science-report.html. (2016)

    2016

  9. [18]

    Naughton, Narasimhan Rampalli, Jude W

    Chaitanya Gokhale, Sanjib Das, AnHai Doan, Jeffrey F. Naughton, Narasimhan Rampalli, Jude W. Shavlik, and Xiaojin Zhu. 2014. Cor- leone: hands-off crowdsourcing for entity matching. In SIGMOD

    2014

  10. [19]

    Halevy, George A

    Behzad Golshan, Alon Y. Halevy, George A. Mihaila, and Wang-Chiew Tan. 2017. Data Integration: After the Teenage Years. In PODS. 101– 106

    2017

  11. [20]

    Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In SIGKDD. ACM, 855–864

    2016

  12. [21]

    Michael Günther. 2018. FREDDY: Fast Word Embeddings in Database Systems. In SIGMOD. ACM, 1817–1819

    2018

  13. [22]

    Michael Günther, Maik Thiele, Erik Nikulski, and Wolfgang Lehner

  14. [23]

    Richard Hull and Masatoshi Yoshikawa. 1990. ILOG: Declarative Cre- ation and Manipulation of Object Identifiers. In VLDB. 455–468

    1990

  15. [24]

    Madelon Hulsebos, Kevin Hu, Michiel Bakker, Emanuel Zgraggen, Arvind Satyanarayan, Tim Kraska, Çagatay Demiralp, and César Hi- dalgo. 2019. Sherlock: A Deep Learning Approach to Semantic Data Type Detection. In SIGKDD. ACM

    2019

  16. [25]

    Jungo Kasai, Kun Qian, Sairam Gurajada, Yunyao Li, and Lucian Popa

  17. [26]

    Christos Koutras, Marios Fragkoulis, Asterios Katsifodimos, and Christoph Lofi. 2020. REMA: Graph Embeddings-based Relational Schema Matching. SEA Data workshop (2020)

    2020

  18. [27]

    Bruno Marnette, Giansalvatore Mecca, Paolo Papotti, Salvatore Rau- nich, and Donatello Santoro. 2011. ++Spicy: an OpenSource Tool for Second-Generation Schema Mapping and Data Exchange. PVLDB 4, 12 (2011), 1438–1441

    2011

  19. [28]

    Sabine Maßmann, Salvatore Raunich, David Aumüller, Patrick Arnold, and Erhard Rahm. 2011. Evolution of the COMA match system. In International Workshop on Ontology Matching

    2011

  20. [29]

    Renée J Miller, Fatemeh Nargesian, Erkang Zhu, Christina Christodoulakis, Ken Q Pu, and Periklis Andritsos. 2018. Mak- ing Open Data Transparent: Data Discovery on Open Data. IEEE Data Eng. Bull. 41, 2 (2018), 59–70

    2018

  21. [30]

    Sidharth Mudgal, Han Li, Theodoros Rekatsinas, AnHai Doan, Young- choon Park, Ganesh Krishnan, Rohit Deep, Esteban Arcaute, and Vijay Raghavendra. 2018. Deep learning for entity matching: A design space exploration. In SIGMOD

    2018

  22. [31]

    Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global Vectors for Word Representation. InEMNLP. 1532–1543

    2014

  23. [32]

    Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christo- pher Clark, Kenton Lee, and Luke Zettlemoyer

    Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christo- pher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextual- ized word representations. CoRR abs/1802.05365 (2018)

    2018 arXiv

  24. [33]

    O’Reilly Media, Inc

    Tye Rattenbury, Joseph M Hellerstein, Jeffrey Heer, Sean Kandel, and Connor Carreras. 2017. Principles of data wrangling: Practical tech- niques for data preparation . " O’Reilly Media, Inc. "

    2017

  25. [34]

    Paul Suganthan, Adel Ardalan, AnHai Doan, and Aditya Akella. 2018. Smurf: Self-Service String Matching Using Random Forests. PVLDB 12, 3 (2018), 278–291

    2018

  26. [35]

    Saravanan Thirumuruganathan, Shameem A Puthiya Parambath, Mourad Ouzzani, Nan Tang, and Shafiq Joty. 2018. Reuse and adap- tation for entity resolution through transfer learning. arXiv preprint arXiv:1809.11084 (2018)

    2018 arXiv

  27. [36]

    Saravanan Thirumuruganathan, Nan Tang, Mourad Ouzzani, and An- Hai Doan. 2020. Data curation with Deep Learning. EDBT (2020)

    2020

  28. [37]

    Joseph Turian, Lev Ratinov, and Yoshua Bengio. 2010. Word represen- tations: a simple and general method for semi-supervised learning. In ACL. ACL, 384–394

    2010

  29. [38]

    Chen Zhao and Yeye He. 2019. Auto-EM: End-to-end Fuzzy Entity- Matching using Pre-trained Deep Models and Transfer Learning. In WWW. 2413–2424

    2019

  30. [39]

    Erkang Zhu, Yeye He, and Surajit Chaudhuri. 2017. Auto-Join: Joining Tables by Leveraging Transformations. PVLDB 10, 10 (2017), 1034– 1045

    2017

  31. [2016]

    CoRR abs/1607.04606 (2016)

    Enriching Word Vectors with Subword Information. CoRR abs/1607.04606 (2016). arXiv:1607.04606 http://arxiv.org/abs/1607. 04606

    2016 arXiv

  32. [2017]

    TACL 5 (2017), 135–146

    Enriching Word Vectors with Subword Information. TACL 5 (2017), 135–146

    2017

  33. [2018]

    arXiv preprint arXiv:1810.04805 (2018)

    Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

    2018 arXiv

  34. [2019]

    arXiv preprint arXiv:1906.08042 (2019)

    Low-resource Deep Entity Resolution with Transfer and Active Learning. arXiv preprint arXiv:1906.08042 (2019)

    2019 arXiv

  35. [2020]

    EDBT (2020)

    RetroLive: Analysis of Relational Retrofitted Word Embeddings. EDBT (2020)

    2020

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