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Time Expression and Named Entity RecognitionTOMN: Constituent-Based Tagging Scheme

Time Expression and Named Entity Recognition: TOMN: Constituent-Based Tagging Scheme [The characteristics of time expressions drive us to design a learning-based method named TOMN to model time expressions. TOMN defines a constituent-based tagging scheme named TOMN scheme with four tags, namely T, O, M, and N, indicating the constituents of time expression, namely Time token, Modifier, Numeral, and the words Outside time expression. In modeling, TOMN assigns a word with a TOMN tag under conditional random fields with minimal features. Essentially, our constituent-based TOMN scheme overcomes the problem of inconsistent tag assignment that is caused by the conventional position-based tagging schemes (e.g., BIO scheme and BILOU scheme). Evaluation shows that TOMN is equally or more effective than state-of-the-art methods on various datasets, and much more robust on cross-datasets.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Time Expression and Named Entity RecognitionTOMN: Constituent-Based Tagging Scheme

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Publisher
Springer International Publishing
Copyright
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
ISBN
978-3-030-78960-2
Pages
59 –75
DOI
10.1007/978-3-030-78961-9_5
Publisher site
See Chapter on Publisher Site

Abstract

[The characteristics of time expressions drive us to design a learning-based method named TOMN to model time expressions. TOMN defines a constituent-based tagging scheme named TOMN scheme with four tags, namely T, O, M, and N, indicating the constituents of time expression, namely Time token, Modifier, Numeral, and the words Outside time expression. In modeling, TOMN assigns a word with a TOMN tag under conditional random fields with minimal features. Essentially, our constituent-based TOMN scheme overcomes the problem of inconsistent tag assignment that is caused by the conventional position-based tagging schemes (e.g., BIO scheme and BILOU scheme). Evaluation shows that TOMN is equally or more effective than state-of-the-art methods on various datasets, and much more robust on cross-datasets.]

Published: Jun 7, 2021

Keywords: Constituent-based tagging scheme; Position-based tagging scheme; Inconsistent tag assignment

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