BIP! Finder - A Morpho-Syntactically Informed LSTM-CRF Model for Named Entity Recognition

2019 • A Morpho-Syntactically Informed LSTM-CRF Model for Named Entity Recognition

Authors: Petya Osenova; Preslav Nakov; Lilia Simeonova; Kiril Simov

Venue: Proceedings - Natural Language Processing in a Deep Learning World

Type: Publication

Abstract: 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 entit... (read more)

Impact:

3.6036543E-9 3.026759E-9 4 4

/ Attention: 0 24

Topics: Natural language processing Artificial intelligence Speech recognition

DOI: 10.26615/978-954-452-056-4_127

External links: Crossref OpenAIRE

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