Year: 2007
Type: conference-paper
Abstract: The identification of occurrences of like and well that serve as discourse markers (DMs) is a classification problem which is studied here on a corpus of dialogue transcripts with more than 4,000 occurrences of each item.Decision trees using item-specific lexical, prosodic, positional and sociolinguistic features are trained using the C4.5 method.The results demonstrate improvement over past experiments, reaching the same range as inter-annotator agreement scores.DM identification appears to benefit from itemspecific classifiers, which perform better than general purpose ones, thanks to the differentiated use of lexical features.
Authors: Andrei Popescu-Belis, Sandrine Zufferey
Institution: University of Geneva
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411117043