Neural sequence labeling for Vietnamese POS Tagging and NER

Autor:	Anh, Duong Nguyen, Kiem, Hieu Nguyen, Van, Vi Ngo
Rok vydání:	2018
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	This paper presents a neural architecture for Vietnamese sequence labeling tasks including part-of-speech (POS) tagging and named entity recognition (NER). We applied the model described in \cite{lample-EtAl:2016:N16-1} that is a combination of bidirectional Long-Short Term Memory and Conditional Random Fields, which rely on two sources of information about words: character-based word representations learned from the supervised corpus and pre-trained word embeddings learned from other unannotated corpora. Experiments on benchmark datasets show that this work achieves state-of-the-art performances on both tasks - 93.52\% accuracy for POS tagging and 94.88\% F1 for NER. Our sourcecode is available at here. Comment: 5 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1811.03754 Zobrazit plný text záznamu View this record from Arxiv