Abstract
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.
Highlights
Named entity recognition (NER), aiming to extract the words or expressions denoting specific entities from documents, is a core research topic in the fields of nature language processing (NLP) and multimedia security
To improve the precision of NER, we propose a method based on a character level bi-directional long-short-term memory network (BiLSTM)
This superior performance confirms that the combination of character vector and sentence vector is beneficial, and this makes it possible to learn deeper semantic features from the text, improving the effectiveness of named entity recognition in the field
Summary
Named entity recognition (NER), aiming to extract the words or expressions denoting specific entities from documents, is a core research topic in the fields of nature language processing (NLP) and multimedia security. It has been extensively investigated in recent years [1] and applied in various scenarios, such as information extraction, dialog system, sentence parsing, machine translation, and metadata annotation. The general named entities studied by academic community are divided into three categories: entity, time, and number. Named entity in text contains rich semantics and is an important semantic unit. There are still many problems to be solved
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