Paper Info
Title
Automatic identification of discourse markers in dialogues: An in-depth study of like and well
Abstract
Abstract: The lexical items like and well can serve as discourse markers (DMs), but can also play numerous other roles, such as verb or adverb. Identifying the occurrences that function as DMs is an important step for language understanding by computers. In this study, automatic classifiers using lexical, prosodic/positional and sociolinguistic features are trained over transcribed dialogues, manually annotated with DM information. The resulting classifiers improve state-of-the-art performance of DM identification, at about 90% recall and 79% precision for like (84.5% accuracy, @k=0.69), and 99% recall and 98% precision for well (97.5% accuracy, @k=0.88). Automatic feature analysis shows that lexical collocations are the most reliable indicators, followed by prosodic/positional features, while sociolinguistic features are marginally useful for the identification of DM like and not useful for well. The differentiated processing of each type of DM improves classification accuracy, suggesting that these types should be treated individually.
Year
DOI
Venue
2011
10.1016/j.csl.2010.12.001
Computer Speech & Language
Keywords
Field
DocType
in-depth study,lexical item,automatic classifier,sociolinguistic feature,dm identification,classification accuracy,automatic identification,automatic feature analysis,discourse marker,differentiated processing,positional feature,dm information,lexical collocation,discourse markers
Verb,Prosody,Computer science,Lexical item,Adverb,Computational linguistics,Speech recognition,Natural language processing,Artificial intelligence,Pattern recognition (psychology),Discourse marker,Collocation
Journal
Volume
Issue
ISSN
25
3
Computer Speech & Language
Citations 
PageRank 
References 
1
0.40
27
Authors
2
Name
Order
Citations
PageRank
Andrei Popescu-Belis157364.13
Sandrine Zufferey2494.98