Robust extraction of subcategorization data from spoken language
Jianguo Li, Chris Brew, Eric Fosler‐Lussier · 2005
Subcategorization data has been crucial for various NLP tasks. Current method for automatic SCF acquisition usually proceeds in two steps: first, generate all SCF cues from a corpus using a parser, and then filter out spurious SCF cues with statistical tests. Previous studies on SCF acquisition have worked mainly with written texts; spoken corpora have received little attention. Transcripts of spoken language pose two challenges absent in written texts: uncertainty about utterance segmentation and disfluency.