Mongolian Named Entity Recognition System with Rich Features

2016-12-01 · COLING 2016 12 · Weihua Wang, Feilong Bao, Guanglai Gao

In this paper, we first build a manually annotated named entity corpus of Mongolian. Then, we propose three morphological processing methods and study comprehensive features, including syllable features, lexical features, context features, morphological features and semantic features in Mongolian named entity recognition. Moreover, we also evaluate the influence of word cluster features on the system and combine all features together eventually. The experimental result shows that segmenting each suffix into an individual token achieves better results than deleting suffixes or using the suffixes as feature. The system based on segmenting suffixes with all proposed features yields benchmark result of F-measure=84.65 on this corpus.

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Tasks

Machine Translationnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Question Answering

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