Towards a Seamless Integration of Word Senses into Downstream NLP Applications

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Authors Nigel Collier, Roberto Navigli, Jose Camacho-Collados, Mohammad Taher Pilehvar
Journal/Conference Name ACL 2017 7
Paper Category
Paper Abstract Lexical ambiguity can impede NLP systems from accurate understanding of semantics. Despite its potential benefits, the integration of sense-level information into NLP systems has remained understudied. By incorporating a novel disambiguation algorithm into a state-of-the-art classification model, we create a pipeline to integrate sense-level information into downstream NLP applications. We show that a simple disambiguation of the input text can lead to consistent performance improvement on multiple topic categorization and polarity detection datasets, particularly when the fine granularity of the underlying sense inventory is reduced and the document is sufficiently large. Our results also point to the need for sense representation research to focus more on in vivo evaluations which target the performance in downstream NLP applications rather than artificial benchmarks.
Date of publication 2017
Code Programming Language Python
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