Modal Sense Classification At Large: Paraphrase-Driven Sense Projection, Semantically Enriched Classification Models and Cross-Genre Evaluations

Ana Marasović, Mengfei Zhou, Alexis Palmer, Anette Frank


Abstract
Modal verbs have different interpretations depending on their context. Their sense categories – epistemic, deontic and dynamic – provide important dimensions of meaning for the interpretation of discourse. Previous work on modal sense classification achieved relatively high performance using shallow lexical and syntactic features drawn from small-size annotated corpora. Due to the restricted empirical basis, it is difficult to assess the particular difficulties of modal sense classification and the generalization capacity of the proposed models. In this work we create large-scale, high-quality annotated corpora for modal sense classification using an automatic paraphrase-driven projection approach. Using the acquired corpora, we investigate the modal sense classification task from different perspectives.
Anthology ID:
2016.lilt-14.3
Volume:
Linguistic Issues in Language Technology, Volume 14, 2016 - Modality: Logic, Semantics, Annotation, and Machine Learning
Month:
Sept
Year:
2016
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LILT
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CSLI Publications
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URL:
https://www.aclweb.org/anthology/2016.lilt-14.3
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http://aclanthology.lst.uni-saarland.de/2016.lilt-14.3.pdf