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A Morpho-Syntactically Informed LSTM-CRF Model for Named Entity Recognition
We propose a morphologically informed model for named entity recognition, which is based on LSTM-CRF architecture and combines word embeddings, Bi-LSTM character embeddings, part-of-speech (POS) tags, and morphological information. While previous work has focused on learning from raw word input, using word and character embeddings only, we show that for morphologically rich languages, such as Bulgarian, access to POS information contributes more to the performance gains than the detailed morphological information. Thus, we show that named entity recognition needs only coarse-grained POS tags, but at the same time it can benefit from simultaneously using some POS information of different granularity. Our evaluation results over a standard dataset show sizable improvements over the state-of-the-art for Bulgarian NER.
Comment: named entity recognition; Bulgarian NER; morphology; morpho-syntax
Microsoft Academic Graph classification: Computer science Artificial intelligence business.industry business Character (mathematics) Morpho biology.organism_classification biology Named-entity recognition computer.software_genre computer Word (computer architecture) Granularity Bulgarian language.human_language language Architecture Natural language processing
Computation and Language (cs.CL), FOS: Computer and information sciences, I.2.7, 68T50, Computer Science - Computation and Language
Computation and Language (cs.CL), FOS: Computer and information sciences, I.2.7, 68T50, Computer Science - Computation and Language
Microsoft Academic Graph classification: Computer science Artificial intelligence business.industry business Character (mathematics) Morpho biology.organism_classification biology Named-entity recognition computer.software_genre computer Word (computer architecture) Granularity Bulgarian language.human_language language Architecture Natural language processing
- UNIVERSITE DE NICE - SOPHIA ANTIPOLIS
- Qatar Computing Research Institute Qatar
- Sofia University Bulgaria
- Bulgarian Academy of Sciences Bulgaria
- Sofia University United States
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Alan Akbik, Tanja Bergmann, and Roland Vollgraf. 2019. Pooled contextualized embeddings for named entity recognition. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis, MN, USA, NAACL-HLT '19, pages 724-728.
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Georgi Georgiev, Preslav Nakov, Kuzman Ganchev, Petya Osenova, and Kiril Simov. 2009. Featurerich named entity recognition for Bulgarian using conditional random fields. In Proceedings of the International Conference on Recent Adcances in Natural Language Processing. Borovets, Bulgaria, RANLP '09, pages 113-117.
Dan Gillick, Cliff Brunk, Oriol Vinyals, and Amarnag Subramanya. 2016. Multilingual language processing from bytes. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. San Diego, CA, USA, NAACLHLT '16, pages 1296-1306. [OpenAIRE]
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- UNIVERSITE DE NICE - SOPHIA ANTIPOLIS
- Qatar Computing Research Institute Qatar
- Sofia University Bulgaria
- Bulgarian Academy of Sciences Bulgaria
- Sofia University United States
- Qatar Computing Research Institute Qatar
- Qatar Foundation Qatar
We propose a morphologically informed model for named entity recognition, which is based on LSTM-CRF architecture and combines word embeddings, Bi-LSTM character embeddings, part-of-speech (POS) tags, and morphological information. While previous work has focused on learning from raw word input, using word and character embeddings only, we show that for morphologically rich languages, such as Bulgarian, access to POS information contributes more to the performance gains than the detailed morphological information. Thus, we show that named entity recognition needs only coarse-grained POS tags, but at the same time it can benefit from simultaneously using some POS information of different granularity. Our evaluation results over a standard dataset show sizable improvements over the state-of-the-art for Bulgarian NER.
Comment: named entity recognition; Bulgarian NER; morphology; morpho-syntax