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Edit Detection and Parsing for Transcribed Speech

By Eugene Charniak and Mark Johnson


We present a simple architecture for parsing transcribed speech in which an edited-word detector first removes such words from the sentence string, and then a standard statistical parser trained on transcribed speech parses the remaining words. The edit detector achieves a misclassification rate on edited words of 2.2%. (The NULL-model, which marks everything as not edited, has an error rate of 5.9%.) To evaluate our parsing results we introduce a new evaluation metric, the purpose of which is to make evaluation of a parse tree relatively indi#erent to the exact tree position of EDITED nodes. By this metric the parser achieves 85.3% precision and 86.5% recall

Year: 2001
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