Multi-Task learning of keyphrase boundary classification

Isabelle Augenstein, A. Sogaard

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

Keyphrase boundary classification (KBC) is the task of detecting keyphrases in scientific articles and labelling them with respect to predefined types. Although important in practice, this task is so far un-derexplored, partly due to the lack of labelled data. To overcome this, we explore several auxiliary tasks, including semantic super-sense tagging and identification of multi-word expressions, and cast the task as a multi-task learning problem with deep recurrent neural networks. Our multi-task models perform significantly better than previous state of the art approaches on two scientific KBC datasets, particularly for long keyphrases
Original languageAmerican English
JournalAssociation for Computational Linguistics
DOIs
StatePublished - 2017

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