11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Combining Word-Based Features, Statistical Language Models, and Parsing for Named Entity Recognition

Joseph Polifroni (1), Stephanie Seneff (2)

(1) Nokia Research Center, USA
(2) MIT, USA

As users become more accustomed to using their mobile devices to organize and schedule their lives, there is more of a demand for applications that can make that process easier. Automatic speech recognition technology has already been developed to enable essentially unlimited vocabulary in a mobile setting. Understanding the words that are spoken is the next challenge. In this paper, we describe efforts to develop a dataset and classifier to recognize named entities in speech. Using sets of both real and simulated data, in conjunction with a very large set of real named entities, we created a challenging corpus of training and test data. We developed a multi-stage framework to parse these utterances and simultaneously tag names and locations. Our combined system achieved an f-measure of 0.87 on extracted proper nouns, with a 95% accuracy on distinguishing names from locations.

Full Paper

Bibliographic reference.  Polifroni, Joseph / Seneff, Stephanie (2010): "Combining word-based features, statistical language models, and parsing for named entity recognition", In INTERSPEECH-2010, 1289-1292.