Abstract
Chinese is a representative East Asian language. Chinese Named Entity Recognition (CNER) aims to recognize various entities. It is significant for other NLP tasks to utilize CNER. Recent research to develop CNER systems has been dedicated to either considering word enhancement or capturing global information to strengthen local composition and alleviate word ambiguity in the meanings of words. However, information on words acquired from external lexicons is often confused, and this has led to incorrect judgments regarding the boundaries of words. Moreover, relevant studies typically use excessively complex models to capture the global semantics of sentences. To solve these two problems, we incorporate a global representation into the procedure of local word enhancement. We propose an intuitive and effective dual-module interactive network that can enhance the boundaries of words and extract the global semantics by using a rethinking mechanism to refine the importance of local composition and global information. The results of experiments on four CNER datasets showed that the proposed model can outperform other baselines in terms of the F1 score.
- [1] . 2005. A shortest path dependency kernel for relation extraction. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT’05). 724–731.Google Scholar
Digital Library
- [2] . 2018. Adversarial transfer learning for Chinese named entity recognition with self-attention mechanism. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP’18). 182–192.Google Scholar
Cross Ref
- [3] . 2017. Graph convolutional networks for named entity recognition. arXiv preprint arXiv:1709.10053 (2017).Google Scholar
- [4] . 2013. Named entity recognition with bilingual constraints. In Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL’13). 52–62.Google Scholar
- [5] . 2006. Chinese named entity recognition with conditional probabilistic models. In Proceedings of the 5th SIGHAN Workshop on Chinese Language Processing. 173–176.Google Scholar
- [6] . 2021. Enhancing entity boundary detection for better Chinese named entity recognition. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 20–25.Google Scholar
Cross Ref
- [7] . 2015. Event extraction via dynamic multi-pooling convolutional neural networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (ACL-IJCNLP’15). 167–176.Google Scholar
Cross Ref
- [8] . 2018. Core techniques of question answering systems over knowledge bases: A survey. Knowledge and Information Systems 55, 3 (2018), 529–569.Google Scholar
Digital Library
- [9] . 2016. Character-based LSTM-CRF with radical-level features for Chinese named entity recognition. In Natural Language Understanding and Intelligent Applications. Springer, 239–250.Google Scholar
Cross Ref
- [10] . 2019. CNN-based Chinese NER with lexicon rethinking. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19). 4982–4988.Google Scholar
Cross Ref
- [11] . 2019. A lexicon-based graph neural network for Chinese NER. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP’19). 1040–1050.Google Scholar
Cross Ref
- [12] . 2016. F-score driven max margin neural network for named entity recognition in Chinese social media. arXiv preprint arXiv:1611.04234 (2016).Google Scholar
- [13] . 2017. A unified model for cross-domain and semi-supervised named entity recognition in Chinese social media. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI’17). 3216–3222.Google Scholar
Cross Ref
- [14] . 2006. The Third International Chinese Language Processing Bakeoff: Word segmentation and named entity recognition. In Proceedings of the 5th SIGHAN Workshop on Chinese Language Processing. 108–117.Google Scholar
- [15] . 2022. Unified named entity recognition as word-word relation classification. Proceedings of the AAAI Conference on Artificial Intelligence 36, 10 (2022), 10965–10973.Google Scholar
Cross Ref
- [16] . 2023. Sequence labeling with meta-learning. IEEE Transactions on Knowledge and Data Engineering 35, 3 (2023), 3072–3086.
DOI: Google ScholarCross Ref
- [17] . 2021. Domain generalization for named entity boundary detection via metalearning. IEEE Transactions on Neural Networks and Learning Systems 32, 9 (2021), 3819–3830.
DOI: Google ScholarCross Ref
- [18] . 2022. A survey on deep learning for named entity recognition. IEEE Transactions on Knowledge and Data Engineering 34, 1 (2022), 50–70.
DOI: Google ScholarDigital Library
- [19] . 2020. FLAT: Chinese NER using flat-lattice transformer. arXiv preprint arXiv:2004.11795 (2020).Google Scholar
- [20] . 2010. Chinese named entity recognition with a sequence labeling approach: Based on characters, or based on words? In Advanced Intelligent Computing Theories and Applications, with Aspects of Artificial Intelligence. Lecture Notes in Computer Science, Vol. 6216. Springer, 634–640.Google Scholar
- [21] . 2016. Multi-prototype Chinese character embedding. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC’16). 855–859.Google Scholar
- [22] . 2014. Word segmentation of overlapping ambiguous strings during Chinese reading. Journal of Experimental Psychology: Human Perception and Performance 40, 3 (2014), 1046.Google Scholar
Cross Ref
- [23] . 2019. Simplify the usage of lexicon in Chinese NER. arXiv preprint arXiv:1908.05969 (2019).Google Scholar
- [24] . 2022. Two languages are better than one: Bilingual enhancement for Chinese named entity recognition. In Proceedings of the 29th International Conference on Computational Linguistics. 2024–2033.Google Scholar
- [25] . 2019. Enhancing local feature extraction with global representation for neural text classification. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP’19). 496–506.Google Scholar
Cross Ref
- [26] . 2015. Named entity recognition for Chinese social media with jointly trained embeddings. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP’15). 548–554.Google Scholar
Cross Ref
- [27] . 2013. Relation extraction with matrix factorization and universal schemas. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 74–84.Google Scholar
- [28] . 2021. Investigating the feasibility of deep learning methods for Urdu word sense disambiguation. Transactions on Asian and Low-Resource Language Information Processing 21, 2 (2021), 1–16.Google Scholar
- [29] . 2021. Joined type length encoding for nested named entity recognition. Transactions on Asian and Low-Resource Language Information Processing 21, 3 (2021), 1–23.Google Scholar
- [30] . 2019. Leverage lexical knowledge for Chinese named entity recognition via collaborative graph network. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP’19). 3830–3840.Google Scholar
Cross Ref
- [31] . 2020. Word-character graph convolution network for Chinese named entity recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing 28 (2020), 1520–1532.Google Scholar
Digital Library
- [32] . 1967. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory 13, 2 (1967), 260–269.Google Scholar
Digital Library
- [33] . 2013. Effective bilingual constraints for semi-supervised learning of named entity recognizers. In Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI’13). 919–925.Google Scholar
Cross Ref
- [34] . 2022. DAMO-NLP at SemEval-2022 Task 11: A knowledge-based system for multilingual named entity recognition. arXiv preprint arXiv:2203.00545 (2022).Google Scholar
- [35] . 2021. A hybrid model for named entity recognition on Chinese electronic medical records. Transactions on Asian and Low-Resource Language Information Processing 20, 2 (2021), 1–12.Google Scholar
Digital Library
- [36] . 2011. OntoNotes Release 4.0. LDC2011T03. Linguistic Data Consortium, Philadelphia, PA.Google Scholar
- [37] . 2021. MECT: Multi-metadata embedding based cross-transformer for Chinese named entity recognition. arXiv preprint arXiv:2107.05418 (2021).Google Scholar
- [38] . 2022. NFLAT: Non-flat-lattice transformer for Chinese named entity recognition. arXiv preprint arXiv:2205.05832 (2022).Google Scholar
- [39] . 2019. TENER: Adapting transformer encoder for named entity recognition. arXiv preprint arXiv:1911.04474 (2019).Google Scholar
- [40] . 2016. Combining discrete and neural features for sequence labeling. In Computational Linguistics and Intelligent Text Processing. Lecture Notes in Computer Science, Vol. 9623. Springer, 140–154.Google Scholar
- [41] . 2006. Word segmentation and named entity recognition for Sighan Bakeoff3. In Proceedings of the 5th SIGHAN Workshop on Chinese Language Processing. 158–161.Google Scholar
- [42] . 2018. Chinese NER using lattice LSTM. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (ACL’18). 1554–1564.Google Scholar
Cross Ref
- [43] . 2021. Chinese named entity recognition based on gated graph neural network. In Knowledge Science, Engineering and Management. Lecture Notes in Computer Science, Vol. 12815. Springer, 604–613.Google Scholar
- [44] . 2019. CAN-NER: Convolutional attention network for Chinese named entity recognition. arXiv preprint arXiv:1904.02141 (2019).Google Scholar
Index Terms
Think More Ambiguity Less: A Novel Dual Interactive Model with Local and Global Semantics for Chinese Named Entity Recognition
Recommendations
Chinese named entity recognition using lexicalized HMMs
Natural language processing and text miningThis paper presents a lexicalized HMM-based approach to Chinese named entity recognition (NER). To tackle the problem of unknown words, we unify unknown word identification and NER as a single tagging task on a sequence of known words. To do this, we ...
Named Entity Recognition and Classification for Punjabi Shahmukhi
Named entity recognition (NER) refers to the identification of proper nouns from natural language text and classifying them into named entity types, such as person, location, and organization. Due to the widespread applications of NER, numerous NER ...
Research on Chinese Named Entity Recognition in the Marine Field
ACAI '18: Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial IntelligenceNamed entity recognition is a basic problem in natural language processing, and also an indispensable part of many natural language processing technologies such as information extraction and information retrieval. There are a large number of proprietary ...






Comments