Named entity recognition for Chinese judgment documents based on BiLSTM and CRF Article Swipe

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Wenming Huang , Dengrui Hu , Zhenrong Deng , Jian‐Yun Nie ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.1186/s13640-020-00539-x

Chinese named entity recognition (CNER) in the judicial domain is an important and fundamental task in the analysis of judgment documents. However, only a few researches have been devoted to this task so far. For Chinese named entity recognition in judgment documents, we propose the use a bidirectional long-short-term memory (BiLSTM) model, which uses character vectors and sentence vectors trained by distributed memory model of paragraph vectors (PV-DM). The output of BiLSTM is used by conditional random field (CRF) to tag the input sequence. We also improved the Viterbi algorithm to increase the efficiency of the model by cutting the path with the lowest score. At last, a novel dataset with manual annotations is constructed. The experimental results on our corpus show that the proposed method is effective not only in reducing the computational time, but also in improving the effectiveness of named entity recognition in the judicial domain.

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Computer Science

Conditional Random Field

Artificial Intelligence

Mathematics

Economics

Management

Mathematical Analysis

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Computer science Conditional random field Named-entity recognition Sentence Task (project management) Artificial intelligence Natural language processing Paragraph Domain (mathematical analysis) Field (mathematics) Hidden Markov model Viterbi algorithm Speech recognition Pattern recognition (psychology) Mathematics Pure mathematics Economics World Wide Web Management Mathematical analysis

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Type: article
Language: en
Landing Page: https://doi.org/10.1186/s13640-020-00539-x
PDF: https://jivp-eurasipjournals.springeropen.com/track/pdf/10.1186/s13640-020-00539-x
OA Status: gold
Cited By: 13
References: 36
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DOI: https://doi.org/10.1186/s13640-020-00539-x

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Title: Named entity recognition for Chinese judgment documents based on BiLSTM and CRF

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Type: article

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Language: en

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Publication year: 2020

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Publication date: 2020-11-23

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Authors: Wenming Huang, Dengrui Hu, Zhenrong Deng, Jian‐Yun Nie

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Landing page: https://doi.org/10.1186/s13640-020-00539-x

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Concepts: Computer science, Conditional random field, Named-entity recognition, Sentence, Task (project management), Artificial intelligence, Natural language processing, Paragraph, Domain (mathematical analysis), Field (mathematics), Hidden Markov model, Viterbi algorithm, Speech recognition, Pattern recognition (psychology), Mathematics, Pure mathematics, Economics, World Wide Web, Management, Mathematical analysis

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Cited by: 13

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