An Extremely Large Vocabulary Approach to Named Entity Extraction from Speech

This paper describes an approach to Named Entity (NE) extraction from speech data, in which an extremely large vocabulary lexicon including all NEs occurring in a large text corpus is used for Automatic Speech Recognition (ASR). Accordingly, NEs appear in the recognition results just as they are. Our approach is implemented by the following steps: (1) run an NE-tagger for a whole text corpus and make an NE-tagged corpus in which each NE is padded with its category, (2) construct a lexicon and a language model for ASR using the tagged corpus where each NE is considered as a regular word, and (3) run the speech recognizer in one pass. Although a very large vocabulary is necessary to ensure a high coverage of NEs, that is no longer a major problem since we recently achieved real-time extremely large vocabulary ASR using a WFST framework. In experiments on NE extraction from spoken queries for an open-domain question-answering system, our approach yielded higher F-measure values than a conventional approach.