Paper Info
Title
Towards Automatic Identification of Discourse Markers in Dialogs: The Case of Like
Abstract
This article discusses the detection of dis- course markers (DM) in dialog transcriptions, by human annotators and by automated means. After a theoretical discussion of the definition of DMs and their relevance to natu- ral language processing, we focus on the role of like as a DM. Results from experiments with human annotators show that detection of DMs is a difficult but reliable task, which re- quires prosodic information from soundtracks. Then, several types of features are defined for automatic disambiguation of like: colloca- tions, part-of-speech tags and duration-based features. Decision-tree learning shows that for like, nearly 70% precision can be reached, with near 100% recall, mainly using colloca- tion filters. Similar results hold for well, with about 91% precision at 100% recall.
Year
Venue
Field
2004
SIGDIAL Workshop
Dialog box,Transcription (linguistics),Computer science,Speech recognition,Natural language processing,Artificial intelligence,Recall,Discourse marker,Collocation
DocType
Citations 
PageRank 
Conference
7
0.68
References 
Authors
2
2
Name
Order
Citations
PageRank
Sandrine Zufferey1494.98
Andrei Popescu-Belis257364.13