Bin Zhang, Alex Marin, Brian Hutchinson, Mari Ostendorf
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引用次数: 18
Abstract
This paper introduces methods to discriminatively learn phrase patterns for use as features in text classification. An efficient solution is described using a recursive algorithm with a mutual information selection criterion. The algorithm automatically determines when word classes are useful in specific locations of a phrase pattern, allowing for variable specificity depending on the amount of labeled data available. Experiments are carried out on three text classification tasks in both English and Chinese, resulting in improved performance when adding the phrase patterns to the existing n-gram features.
期刊介绍:
The IEEE Transactions on Audio, Speech and Language Processing covers the sciences, technologies and applications relating to the analysis, coding, enhancement, recognition and synthesis of audio, music, speech and language. In particular, audio processing also covers auditory modeling, acoustic modeling and source separation. Speech processing also covers speech production and perception, adaptation, lexical modeling and speaker recognition. Language processing also covers spoken language understanding, translation, summarization, mining, general language modeling, as well as spoken dialog systems.