Abstract
In this paper, we propose a novel co-occurrence probabilities based similarity measure for inducing semantic classes. Clustering with the new similarity measure outperforms the widely used distance based on Kullback-Leibler divergence in precision, recall and F1 evaluation. In our experiments, we induced semantic classes from unannotated in-domain corpus and then used the induced classes and structures to generate large in-domain corpus which was then used for language model adaptation. Character recognition rate was improved from 85.2% to 91%. We imply a new measure to solve the lack of domain data problem by first induction then generation for a dialogue system.
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This work is partially supported by the National Natural Science Foundation of China under Grant Nos. 10925419, 90920302, 10874203, 60875014, 61072124, 11074275, 11161140319.
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Li, YL., Xu, WQ. & Yan, YH. A Novel Similarity Measure to Induce Semantic Classes and Its Application for Language Model Adaptation in a Dialogue System. J. Comput. Sci. Technol. 27, 443–450 (2012). https://doi.org/10.1007/s11390-012-1233-0
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DOI: https://doi.org/10.1007/s11390-012-1233-0