Paper: Semi-Supervised Semantic Tagging of Conversational Understanding using Markov Topic Regression

ACL ID P13-1090
Title Semi-Supervised Semantic Tagging of Conversational Understanding using Markov Topic Regression
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

Finding concepts in natural language ut- terances is a challenging task, especially given the scarcity of labeled data for learn- ing semantic ambiguity. Furthermore, data mismatch issues, which arise when the expected test (target) data does not exactly match the training data, aggra- vate this scarcity problem. To deal with these issues, we describe an efficient semi- supervised learning (SSL) approach which has two components: (i) Markov Topic Regression is a new probabilistic model to cluster words into semantic tags (con- cepts). It can efficiently handle seman- tic ambiguity by extending standard topic models with two new features. First, it en- codes word n-gram features from labeled source and unlabeled target data. Sec- ond, by going beyond a bag-of-words ap- proach, it takes into...