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De-Conflated Semantic Representations

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Collier, NH 
Pilehvar, Mohammad Taher 


One major deficiency of most semantic representation techniques is that they usually model a word type as a single point in the semantic space, hence conflating all the meanings that the word can have. Addressing this issue by learning distinct representations for individual meanings of words has been the subject of several research studies in the past few years. However, the generated sense representations are either not linked to any sense inventory or are unreliable for infrequent word senses. We propose a technique that tackles these problems by de-conflating the representations of words based on the deep knowledge that can be derived from a semantic network. Our approach provides multiple advantages in comparison to the previous approaches, including its high coverage and the ability to generate accurate representations even for infrequent word senses. We carry out evaluations on six datasets across two semantic similarity tasks and report state-of-the-art results on most of them.



Journal Title

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

Conference Name

2016 Conference on Empirical Methods in Natural Language Processing

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Volume Title


Association for Computational Linguistics
Medical Research Council (MR/M025160/1)
Engineering and Physical Sciences Research Council (EP/M005089/1)