Empirical Study of Named Entity Recognition Performance Using Distribution-aware Word Embedding
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arXiv
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| Format: | Preprint |
| Published: |
2021
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| _version_ | 1866914646313664512 |
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| author | Chen, Xin Zhao, Qi Liu, Xinyang |
| author_facet | Chen, Xin Zhao, Qi Liu, Xinyang |
| contents | With the fast development of Deep Learning techniques, Named Entity Recognition (NER) is becoming more and more important in the information extraction task. The greatest difficulty that the NER task faces is to keep the detectability even when types of NE and documents are unfamiliar. Realizing that the specificity information may contain potential meanings of a word and generate semantic-related features for word embedding, we develop a distribution-aware word embedding and implement three different methods to make use of the distribution information in a NER framework. And the result shows that the performance of NER will be improved if the word specificity is incorporated into existing NER methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2109_01636 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Empirical Study of Named Entity Recognition Performance Using Distribution-aware Word Embedding Chen, Xin Zhao, Qi Liu, Xinyang Computation and Language Machine Learning With the fast development of Deep Learning techniques, Named Entity Recognition (NER) is becoming more and more important in the information extraction task. The greatest difficulty that the NER task faces is to keep the detectability even when types of NE and documents are unfamiliar. Realizing that the specificity information may contain potential meanings of a word and generate semantic-related features for word embedding, we develop a distribution-aware word embedding and implement three different methods to make use of the distribution information in a NER framework. And the result shows that the performance of NER will be improved if the word specificity is incorporated into existing NER methods. |
| title | Empirical Study of Named Entity Recognition Performance Using Distribution-aware Word Embedding |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2109.01636 |