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This paper explores several unsupervised approaches to automatic keyword extraction using meeting transcripts. In the TFIDF (term frequency, inverse document frequency) weighting framework, we incorporated part-of-speech (POS) information, word clustering, and sentence salience score. We also evaluated a graph-based approach that measures the importance of(More)
This paper describes a two-phase method for expanding abbreviations found in informal text (e.g., email, text messages, chat room conversations) using a machine translation system trained at the character level during the first phase. In this way, the system learns mappings between character-level " phrases " and is much more robust to new abbreviations(More)
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