Automatic parsing of parental verbal input.
To evaluate theoretical proposals regarding the course of child language acquisition, researchers often need to rely on the processing of large numbers of syntactically parsed utterances, both from children and from their parents. Because it is so difficult to do this by hand, there are currently no parsed corpora of child language input data. To automate this process, we developed a system that combined the MOR tagger, a rule-based parser, and statistical disambiguation techniques. The resultant system obtained nearly 80% correct parses for the sentences spoken to children. To achieve this level, we had to construct a particular processing sequence that minimizes problems caused by the coverage/ambiguity tradeoff in parser design. These procedures are particularly appropriate for use with the CHILDES database, an international corpus of transcripts. The data and programs are now freely available over the Internet.
Sagae
Kenji
K
Language Technologies Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA. sagae@cs.cmu.edu
MacWhinney
Brian
B
Lavie
Alon
A
D000328:Adult; D002648:Child; D002675:Child, Preschool; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D003663:Decision Trees; D006801:Humans; D007804:Language Development; D008960:Models, Psychological; D009323:Natural Language Processing; D010290:Parents; D011578:Psycholinguistics / Q000379:methods*; D012984:Software; D012985:Software Design; D013060:Speech / Q000145:classification*; D014706:Verbal Learning