Konferenzpaper | | Not reviewed Open Access

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

Autor(en)
Hartung, Matthias ; Kaupmann, Fabian ; Jebbara, Soufian ; Cimiano, Philipp 0000-0002-4771-441X
Titel des Konferenzbandes
Proceedings of the 15th Meeting of the European Chapter of the Association for Computational Linguistics (EACL)
Name der Konferenz
EACL 2017
URN
urn:nbn:de:0070-pub-29078593
URL
https://pub.uni-bielefeld.de/record/2907859

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.

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