Named entity recognition using an HMM-based chunk tagger
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Automatic tagging and geotagging in video collections and communities
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Mining automatic speech transcripts for the retrieval of problematic calls
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
Spontaneous speech and opinion detection: mining call-centre transcripts
Language Resources and Evaluation
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This paper focuses on the detection of business concepts in call-centre conversation transcriptions. In the literature, information extraction behavior has been rarely deeply analyzed on such spontaneous speech data. We highlight here the various problems that are encountered when we attempt to extract information from such data. The recall and precision, which are obtained by comparing the concept detection method on automatic vs. manual transcription, are respectively at 74.8% and 77.7%. We find that, even though the concept detection is similar on the whole between manual and automatic transcriptions, spontaneous speech features tend to cause different behaviors of opinion-related concept detection on both transcriptions. On the one hand, spontaneous speech features, which frequently occur in these data, provokes silence (lack of detection) when detecting concepts on both transcriptions. On the other hand, ASR errors (e.g. due to homophony or disfluencies) tend to provoke noise (excessive detection) when detecting concept on automatic transcription.