| Autor: |
Hirschberg, Julia Bell, Gravano, Agustin, Benus, Stefan, Mitchell, Shira, Vovsha, Ilia |
| Rok vydání: |
2007 |
| Předmět: |
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| DOI: |
10.7916/d81c25bf |
| Popis: |
We present results of a series of machine learning experiments that address the classification of the discourse function of single affirmative cue words such as alright, okay and mm-hm in a spoken dialogue corpus. We suggest that a simple discourse/sentential distinction is not sufficient for such words and propose two additional classification sub-tasks: identifying (a) whether such words convey acknowledgment or agreement, and (b) whether they cue the beginning or end of a discourse segment. We also study the classification of each individual word into its most common discourse functions. We show that models based on contextual features extracted from the time-aligned transcripts approach the error rate of trained human aligners. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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