Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155193
Title: Extracting time expressions and named entities with constituent-based tagging schemes
Authors: Zhong, Xiaoshi
Cambria, Erik
Hussain Amir
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Zhong, X., Cambria, E. & Hussain Amir (2020). Extracting time expressions and named entities with constituent-based tagging schemes. Cognitive Computation, 12(4), 844-862. https://dx.doi.org/10.1007/s12559-020-09714-8
Project: A18A2b0046
Journal: Cognitive Computation
Abstract: Time expressions and named entities play important roles in data mining, information retrieval, and natural language processing. However, the conventional position-based tagging schemes (e.g., the BIO and BILOU schemes) that previous research used to model time expressions and named entities suffer from the problem of inconsistent tag assignment. To overcome the problem of inconsistent tag assignment, we designed a new type of tagging schemes to model time expressions and named entities based on their constituents. Specifically, to model time expressions, we defined a constituent-based tagging scheme termed TOMN scheme with four tags, namely T, O, M, and N, indicating the defined constituents of time expressions, namely time token, modifier, numeral, and the words outside time expressions. To model named entities, we defined a constituent-based tagging scheme termed UGTO scheme with four tags, namely U, G, T, and O, indicating the defined constituents of named entities, namely uncommon word, general modifier, trigger word, and the words outside named entities. In modeling, our TOMN and UGTO schemes model time expressions and named entities under conditional random fields with minimal features according to an in-depth analysis for the characteristics of time expressions and named entities. Experiments on diverse datasets demonstrate that our proposed methods perform equally with or more effectively than representative state-of-the-art methods on both time expression extraction and named entity extraction.
URI: https://hdl.handle.net/10356/155193
ISSN: 1866-9956
DOI: 10.1007/s12559-020-09714-8
Rights: © 2020 Springer Science+Business Media, LLC, part of Springer Nature. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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