Incorporating Global Contexts into Sentence Embedding for Relational Extraction at the Paragraph Level with Distant Supervision

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The increased demand for structured knowledge has created considerable interest in relation extraction (RE) from large collections of documents. In particular, distant supervision can be used for RE without manual annotation costs. Nevertheless, this paradigm only extracts relations from individual sentences that contain two target entities. This paper explores the incorporation of global contexts derived from paragraph-into-sentence embedding as a means of compensating for the shortage of training data in distantly supervised RE. Experiments on RE from Korean Wikipedia show that the presented approach can learn an exact RE from sentences (including grammatically incoherent sentences) without syntactic parsing.
Publisher
European Language Resources Association (ELRA)
Issue Date
2018-05-07
Language
English
Citation

11th International Conference on Language Resources and Evaluation, LREC 2018, pp.3562 - 3566

URI
http://hdl.handle.net/10203/276335
Appears in Collection
CS-Conference Papers(학술회의논문)
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