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Extracting Time Expressions and Named Entities with Constituent-Based Tagging Schemes

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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.

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Notes

  1. In a supervised-learning procedure, tag assignment occurs in two stages: (1) feature extraction in the training stage and (2) tag prediction in the testing stage. We focus on the training stage to analyze the impact of tag assignment.

  2. OntoNotes5’s 18 entity types include CARDINAL, DATE, EVENT, FAC, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK_OF_ART.

  3. Those removed entity types are CARDINAL, DATE, MONEY, ORDINAL, PERCENT, QUANTITY, TIME.

  4. https://github.com/ontonotes/conll-formatted-ontonotes-5.0

  5. The pwhole of proper nouns does not reach 100% mainly because each individual dataset is concerned with certain types of named entities and partly because some NNP* words are POS tagging errors, e.g., “SURPRISE DEFEAT” is tagged as “NNPNNP,” but it should be tagged as “JJ NN.”

  6. The BIO scheme in this paper denotes the standard IOB2 scheme described in [67].

  7. The BILOU scheme is also widely known as the BIOES or IOBES scheme.

  8. https://en.wikipedia.org/wiki/Lists_of_cities_by_country and https://en.wikipedia.org/wiki/Lists_of_people_by_nationality.

  9. Note that this kind of uncommon words are not available in the training phase because they are extracted from the unannotated test set.

  10. We followed [82] not to use the Gigaword dataset in experiments because its labels are not ground-truth labels, but are automatically generated by other taggers.

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Funding

This research was funded by the AME Programmatic Funding (Project No. A18A2b0046) from the Agency for Science, Technology and Research (A*STAR), Singapore.

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  1. School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore

    Xiaoshi Zhong & Erik Cambria

  2. School of Computing, Edinburgh Napier University, Scotland, UK

    Amir Hussain

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  1. Xiaoshi Zhong
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Correspondence to Xiaoshi Zhong.

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This paper is an extension of the following conference paper: Xiaoshi Zhong and Erik Cambria. 2018. Time Expression Recognition Using a Constituent-based Tagging Scheme. In Proceedings of the 2018 World Wide Web Conference , Association for Computing Machinery, Lyon, France, pages 983–992.

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Zhong, X., Cambria, E. & Hussain, A. Extracting Time Expressions and Named Entities with Constituent-Based Tagging Schemes. Cogn Comput 12, 844–862 (2020). https://doi.org/10.1007/s12559-020-09714-8

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