Learning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases

Hartung M, Kaupmann F, Jebbara S, Cimiano P (2017)
In: Proceedings of the 15th Meeting of the European Chapter of the Association for Computational Linguistics (EACL)., Vol. 1 (Long Papers). 54-64.

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Conference Paper | Published | English
Abstract
Word embeddings have been shown to be highly effective in a variety of lexical semantic tasks. They tend to capture meaningful relational similarities between individual words, at the expense of lacking the capabilty of making the underlying semantic relation explicit. In this paper, we investigate the attribute relation that often holds between the constituents of adjective-noun phrases. We use CBOW word embeddings to represent word meaning and learn a compositionality function that combines the individual constituents into a phrase representation, thus capturing the compositional attribute meaning. The resulting embedding model, while being fully interpretable, outperforms count- based distributional vector space models that are tailored to attribute meaning in the two tasks of attribute selection and phrase similarity prediction. Moreover, as the model captures a generalized layer of attribute meaning, it bears the potential to be used for predictions over various attribute inventories without re-training.
Publishing Year
Conference
EACL 2017
Location
Valencia, Spain
Conference Date
2017-04-03 – 2017-04-07
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Hartung M, Kaupmann F, Jebbara S, Cimiano P. Learning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases. In: Proceedings of the 15th Meeting of the European Chapter of the Association for Computational Linguistics (EACL). Vol Vol. 1 (Long Papers). 2017: 54-64.
Hartung, M., Kaupmann, F., Jebbara, S., & Cimiano, P. (2017). Learning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases. Proceedings of the 15th Meeting of the European Chapter of the Association for Computational Linguistics (EACL), Vol. 1 (Long Papers), 54-64.
Hartung, M., Kaupmann, F., Jebbara, S., and Cimiano, P. (2017). “Learning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases” in Proceedings of the 15th Meeting of the European Chapter of the Association for Computational Linguistics (EACL), vol. Vol. 1 (Long Papers), 54-64.
Hartung, M., et al., 2017. Learning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases. In Proceedings of the 15th Meeting of the European Chapter of the Association for Computational Linguistics (EACL). no.Vol. 1 (Long Papers) pp. 54-64.
M. Hartung, et al., “Learning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases”, Proceedings of the 15th Meeting of the European Chapter of the Association for Computational Linguistics (EACL), vol. Vol. 1 (Long Papers), 2017, pp.54-64.
Hartung, M., Kaupmann, F., Jebbara, S., Cimiano, P.: Learning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases. Proceedings of the 15th Meeting of the European Chapter of the Association for Computational Linguistics (EACL). Vol. 1 (Long Papers), p. 54-64. (2017).
Hartung, Matthias, Kaupmann, Fabian, Jebbara, Soufian, and Cimiano, Philipp. “Learning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases”. Proceedings of the 15th Meeting of the European Chapter of the Association for Computational Linguistics (EACL). 2017.Vol. Vol. 1 (Long Papers). 54-64.
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