Abstract
Adjective understanding is crucial for answering qualitative or subjective questions, such as “is New York a big city”, yet not as sufficiently studied as answering factoid questions. Our goal is to project adjectives in the continuous distributional space, which enables to answer not only the qualitative question example above, but also comparative ones, such as “is New York bigger than San Francisco?”. As a basis, we build on the probability P(New York—big city) and P(Boston—big city) observed in Hearst patterns from a large Web corpus (as captured in a probabilistic knowledge base such as Probase). From this base model, we observe that this probability well predicts the graded score of adjective, but only for “head entities” with sufficient observations. However, the observation of a city is scattered to many adjectives – Cities are described with 194 adjectives in Probase, and, on average, only 2% of cities are sufficiently observed in adjective-modified concepts. Our goal is to train a distributional model such that any entity can be associated to any adjective by its distance from the vector of ‘big city’ concept. To overcome sparsity, we learn highly synonymous adjectives, such as big and huge cities, to improve prediction accuracy. We validate our finding with real-word knowledge bases.
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References
Bagherinezhad, H., Hajishirzi, H., Choi, Y., Farhadi, A.: Are elephants bigger than butterflies? Reasoning about sizes of objects. arXiv preprint arXiv:1602.00753 (2016)
Bian, J., Gao, B., Liu, T.-Y.: Knowledge-powered deep learning for word embedding. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8724, pp. 132–148. Springer, Heidelberg (2014). doi:10.1007/978-3-662-44848-9_9
Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: Conference on Artificial Intelligence (2011)
Day, W.H., Edelsbrunner, H.: Efficient algorithms for agglomerative hierarchical clustering methods. J. Classif. 1(1), 7–24 (1984)
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391 (1990)
Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the Eighth Conference on European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics (1997)
He, Y., Chakrabarti, K., Cheng, T., Tylenda, T.: Automatic discovery of attribute synonyms using query logs and table corpora. In: International World Wide Web Conferences Steering Committee, WWW (2016)
Huang, P.-S., He, X., Gao, J., Deng, L., Acero, A., Heck, L.: Learning deep structured semantic models for web search using clickthrough data. In: CIKML. ACM (2013)
Iwanari, T., Yoshinaga, N., Kaji, N., Nishina, T., Toyoda, M., Kitsuregawa, M.: Ordering concepts based on common attribute intensity. In: IJCAI (2016)
Lee, T., Wang, Z., Wang, H., Hwang, S.-W.: Attribute extraction and scoring: a probabilistic approach. In: ICDE. IEEE (2013)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: AAAI (2015)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Mikolov, T., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems (2013)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP (2014)
Rothe, S., Schütze, H.: Autoextend: extending word embeddings to embeddings for synsets and lexemes. arXiv preprint arXiv:1507.01127 (2015)
Tandon, N., de Melo, G., Suchanek, F., Weikum, G.: Webchild: harvesting and organizing commonsense knowledge from the web. In: WSDM. ACM (2014)
Trummer, I., Halevy, A., Lee, H., Sarawagi, S., Gupta, R.: Mining subjective properties on the web. In: SIGMOD. ACM (2015)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: AAAI. Citeseer (2014)
Wu, W., Li, H., Wang, H., Zhu, K.Q.: Probase: a probabilistic taxonomy for text understanding. In: SIGMOD. ACM (2012)
Acknowledgment
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. B0101-16-0307, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center)).
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Lee, K., Cho, H., Hwang, Sw. (2017). Gradable Adjective Embedding for Commonsense Knowledge. 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 10235. Springer, Cham. https://doi.org/10.1007/978-3-319-57529-2_63
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DOI: https://doi.org/10.1007/978-3-319-57529-2_63
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