Мақола

Using N-best lists for named entity recognition from Chinese speech

Lu-Feng ZhaiHKUST Human Language Technology Center Electrical & Electronic Engineering University of Science and Technology Clear Water Bay, Hong KongPascale FungHKUST Human Language Technology Center Electrical & Electronic Engineering University of Science and Technology Clear Water Bay, Hong KongRichard SchwartzBBN Technologies 9861 Broken Land Parkway Columbia, MD 21046 U.S.AMarine CarpuatHKUST Human Language Technology Center Department of Computer Science University of Science and Technology Clear Water Bay, Hong KongDekai WuHKUST Human Language Technology Center Department of Computer Science University of Science and Technology Clear Water Bay, Hong Kong

2004en

ABI

Аннотация

We present the first known result for named entity recognition (NER) in realistic largevocabulary spoken Chinese. We establish this result by applying a maximum entropy model, currently the single best known approach for textual Chinese NER, to the recognition output of the BBN LVCSR system on Chinese Broadcast News utterances. Our results support the claim that transferring NER approaches from text to spoken language is a significantly more difficult task for Chinese than for English. We propose re-segmenting the ASR hypotheses as well as applying postclassification to improve the performance. Finally, we introduce a method of using n-best hypotheses that yields a small but nevertheless useful improvement NER accuracy. We use acoustic, phonetic, language model, NER and other scores as confidence measure. Experimental results show an average of 6.7% relative improvement in precision and 1.7% relative improvement in F-measure.

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Мавзулар

Topic Modeling Natural Language Processing Techniques Speech Recognition and Synthesis

Идентификаторлар

DOI: 10.3115/1613984.1613994

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