An Evaluation of POS Taggers for the CHILDES Corpus
This project evaluates four mainstream taggers on a representative collection of child-adult’s dialogues from Child Language Data Exchange System. The nine children’s files from Valian corpora and part of Eve corpora have been manually labeled, and rewrote with LARC tagset. They served as gold standard corpora in the training and testing process. Four taggers: CLAN MOR tagger, ACOPOST trigram tagger, Stanford parser, and Ver. 1.14 of Brill tagger have been tested by 10-fold cross validation. By analyzing what kinds of assumptions the tagger made about category assignment lead to failing, we identify several problematic cases of tagging. By comparing the average error rate of each tagger, we found the size of training data set, and the length of utterance both plays a role to effect tagging accuracy.
Computational Linguistics, First and Second Language Acquisition, Part-of-speech, Tagger, Tagging, MLU, Tagging Accuracy
City University of New York - Graduate Center
Networked Digital Library of Theses and Dissertations
The Graduate School and University Center of The City University of New York