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Feature-Less End-to-End Nested Term Extraction

Authors :
Gao, Yuze
Yuan, Yu
Source :
NLPCC XAI 2019
Publication Year :
2019

Abstract

In this paper, we proposed a deep learning-based end-to-end method on the domain specified automatic term extraction (ATE), it considers possible term spans within a fixed length in the sentence and predicts them whether they can be conceptual terms. In comparison with current ATE methods, the model supports nested term extraction and does not crucially need extra (extracted) features. Results show that it can achieve high recall and a comparable precision on term extraction task with inputting segmented raw text.

Details

Database :
arXiv
Journal :
NLPCC XAI 2019
Publication Type :
Report
Accession number :
edsarx.1908.05426
Document Type :
Working Paper