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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2017: Advances in Knowledge Discovery and Data Mining pp 135–146Cite as

Distributed Representations for Words on Tables

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10234))

Abstract

We consider a problem of word embedding for tables, and we obtain distributed representations for words found in tables. We propose a table word-embedding method, which considers both horizontal and vertical relations between cells to estimate appropriate word embedding for words in tables. We propose objective functions that make use of horizontal and vertical relations, both individually and jointly.

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Notes

  1. 1.

    The total number of tables found in the corpus was 255,039.

  2. 2.

    In our data set, 266 (93.7%) out of 284 randomly sampled tables were row-wise.

  3. 3.

    In this research, we ignore tables that have no attribute names. Although this strategy can cause noise in the set of attribute vectors, the effects of such noise are small, because values are of many types and their frequency is relatively lower than that of the attributes.

  4. 4.

    The original paper of word2vec derived this objective by maximizing the probability of a word appearing in the given contexts, but here we ignore these derivations and consider only the following objectives as merely the score function for the purpose of obtaining word-embedding vectors.

  5. 5.

    In addition, note that only two of these four terms are used for each (w, z) pair, rendering the SGD implementation for this model nearly the same as that of word2vec.

  6. 6.

    Although two (the first and second) terms are used for the word w and its vertical context word c, we can differentiate each term independently because there are no vectors appearing both of the terms, thus we can use the iteration method similar to that of word2vec.

  7. 7.

    Note that as a result, the size of the similarity and analogy task queries was reduced to 445 and 5,124, respectively.

References

  1. Bollegala, D., Alsuhaibani, M., Maehara, T., Kawarabayashi, K.I.: Joint word representation learning using a corpus and a semantic lexicon. In: Proceedings of AAAI 2016, pp. 2690–2696 (2016)

    Google Scholar 

  2. Bollegala, D., Maehara, T., Yoshida, Y., Kawarabayashi, K.I.: Learning word representations from relational graphs. In: Proceedings of AAAI 2015, pp. 2146–2152 (2015)

    Google Scholar 

  3. Cafarella, M.J., Halevy, A.Y., Wang, D.Z., Wu, E., Zhang, Y.: Webtables: exploring the power of tables on the web. Proc. VLDB Endowment 1(1), 538–549 (2008)

    Article  Google Scholar 

  4. Chakrabarti, S.: Mining the Web: Discovering Knowledge from Hypertext Data. Morgan-Kaufmann Publishers, Burlington (2002)

    Google Scholar 

  5. Embley, D., Hurst, M., Lopresti, D., Nagy, G.: Table-processing paradigms: a research survey. Int. J. Doc. Anal. Recogn. 8(2), 66–86 (2006)

    Article  Google Scholar 

  6. Ji, S., Satish, N., Li, S., Dubey, P.: Parallelizing word2vec in shared and distributed memory. CoRR abs/ 1604.04661 (2016)

    Google Scholar 

  7. Limaye, G., Sarawagi, S., Chakrabarti, S.: Annotating and searching web tables using entities, types and relationships. Proc. VLDB Endowment 3(1), 1338–1347 (2010)

    Article  Google Scholar 

  8. Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of AAAI 2015, pp. 2181–2187 (2015)

    Google Scholar 

  9. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS 2013, pp. 3111–3119 (2013)

    Google Scholar 

  10. Munoz, E., Hogan, A., Mileo, A.: Triplifying Wikipedia’s tables. In: Proceedings of the ISWC 2013 Workshop on Linked Data for Information Extraction (2013)

    Google Scholar 

  11. Neelakantan, A., Roth, B., McCallum, A.: Compositional vector space models for knowledge base completion. In: Proceedings of ACL 2015, pp. 156–166 (2015)

    Google Scholar 

  12. Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings of EMNLP 2014, pp. 1532–1543 (2014)

    Google Scholar 

  13. Pimplikar, R., Sarawagi, S.: Answering table queries on the web using column keywords. Proc. VLDB Endowment 5(10), 908–919 (2012)

    Article  Google Scholar 

  14. Recht, B., Re, C., Wright, S.J., Niu, F.: Hogwild: a lock-free approach to parallelizing stochastic gradient descent. In: Proceedings of NIPS 2011, pp. 693–701 (2011)

    Google Scholar 

  15. Toutanova, K., Chen, D., Pantel, P., Poon, H., Choudhury, P., Gamon, M.: Representing text for joint embedding of text and knowledge bases. In: Proceedings of EMNLP 2015, pp. 1499–1509 (2015)

    Google Scholar 

  16. Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of AAAI 2014, pp. 1112–1119 (2014)

    Google Scholar 

  17. Yin, P., Lu, Z., Li, H., Kao, B.: Neural enquirer: learning to query tables in natural language. In: Proceedings of IJCAI 2016, pp. 2308–2314 (2016)

    Google Scholar 

  18. Zanibbi, R., Blostein, D., Cordy, J.R.: A survey of table recognition. Int. J. Doc. Anal. Recogn. 7(1), 1–16 (2004)

    Article  Google Scholar 

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Acknowledgement

This work was supported by JSPS KAKENHI Grant Numbers JP15K00309, JP15K00425, JP15K16077.

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Authors and Affiliations

  1. Institute of Technology and Science, University of Tokushima, 2-1, Minami-josanjima, Tokushima, 770-8506, Japan

    Minoru Yoshida, Kazuyuki Matsumoto & Kenji Kita

Authors
  1. Minoru Yoshida
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  2. Kazuyuki Matsumoto
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  3. Kenji Kita
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Corresponding author

Correspondence to Minoru Yoshida .

Editor information

Editors and Affiliations

  1. Kangwon National University, Chuncheon, Korea (Republic of)

    Jinho Kim

  2. Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  3. University of Technology Sydney, Sydney, New South Wales, Australia

    Longbing Cao

  4. KAIST, Daejeon, Korea (Republic of)

    Jae-Gil Lee

  5. University of New South Wales, Sydney, New South Wales, Australia

    Xuemin Lin

  6. Kangwon National University, Chuncheon, Korea (Republic of)

    Yang-Sae Moon

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Yoshida, M., Matsumoto, K., Kita, K. (2017). Distributed Representations for Words on Tables. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_11

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  • DOI: https://doi.org/10.1007/978-3-319-57454-7_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57453-0

  • Online ISBN: 978-3-319-57454-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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