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Automatic keyword extraction for the meeting corpus using supervised approach and bigram expansion (2008)
| Content Provider | CiteSeerX |
|---|---|
| Author | Liu, Fei Liu, Yang Liu, Feifan |
| Abstract | In this paper, we tackle the problem of automatic keyword extraction in the meeting domain, a genre significantly different from written text. For the supervised framework, we proposed a rich set of fea-tures beyond the typical TFIDF measures, such as sentence salience weight, lexical features, summary sentences, and speaker informa-tion. We also evaluate different candidate sampling approaches for better model training and testing. In addition, we introduced a bi-gram expansion module which aims at extracting “entity bigrams” using Web resources. Using the ICSI meeting corpus, we demon-strate the effectiveness of the features and show that the supervised method and the bigram expansion module outperform the unsuper-vised TFIDF selection with POS (part-of-speech) filtering. Finally, we show the approaches introduced in this paper perform well on the speech recognition output. |
| File Format | |
| Journal | Proceedings of the IEEE Workshop on Spoken Language Technology (IEEE SLT |
| Publisher Date | 2008-01-01 |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Proceeding Conference Proceedings Article |
