An Extremely Large Vocabulary Approach to Named Entity Extraction from Speech
Takaaki Hori, Atsushi Nakamura · 2006
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 WEST 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