A Character-Level BiLSTM-CRF Model With Multi-Representations for Chinese Event Detection Article Swipe

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YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.1109/access.2019.2943721

Using the word as a basic unit may undermine Chinese event detection model’s performance because of the inaccurate word boundaries generated by segmentation tools. Besides, word embeddings are contextual independent and cannot handle the polysemy of event triggers, which may prevent us from obtaining the desired performance. To address these issues, we propose a BiLSTM-CRF (Bidirectional Long Short-Term Memory Conditional Random Field) model using contextualized representations, which regards event detection task as a character-level sequence labeling problem and uses contextualized representations to disambiguate event triggers. Experiments show that our proposed method sets a new state-of-the-art, which proves Chinese characters could replace words for the Chinese event detection task. Besides, using contextualized representation reduces the false positive case, which verifies that this kind of representation could remedy the weakness of the word embedding technique. Based on the results, we believe that character-level models are worth exploring in the future.

Related Topics

Computer Science

Conditional Random Field

Polysemy

Artificial Intelligence

Concepts

Computer science Conditional random field Character (mathematics) Polysemy Event (particle physics) Task (project management) Artificial intelligence Sequence labeling Word (group theory) Natural language processing Representation (politics) Embedding Field (mathematics) Linguistics Mathematics Economics Politics Management Geometry Law Political science Quantum mechanics Physics Pure mathematics Philosophy

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Type: article
Language: en
Landing Page: https://doi.org/10.1109/access.2019.2943721
PDF: https://ieeexplore.ieee.org/ielx7/6287639/8600701/08848381.pdf
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Cited By: 9
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DOI: https://doi.org/10.1109/access.2019.2943721

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Title: A Character-Level BiLSTM-CRF Model With Multi-Representations for Chinese Event Detection

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Language: en

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Publication year: 2019

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Publication date: 2019-01-01

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Authors: Xiaofeng Mu, Aiping Xu

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Landing page: https://doi.org/10.1109/access.2019.2943721

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Concepts: Computer science, Conditional random field, Character (mathematics), Polysemy, Event (particle physics), Task (project management), Artificial intelligence, Sequence labeling, Word (group theory), Natural language processing, Representation (politics), Embedding, Field (mathematics), Linguistics, Mathematics, Economics, Politics, Management, Geometry, Law, Political science, Quantum mechanics, Physics, Pure mathematics, Philosophy

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Cited by: 9

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