A Computationally Efficient Algorithm for Learning Topical Collocation Models

Zhendong Zhao, Lan Du, Benjamin Börschinger, John K. Pate, Massimiliano Ciaramita, Mark Steedman, Mark Johnson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Most existing topic models make the bag of-words assumption that words are generated independently, and so ignore potentially useful information about word order.Previous attempts to use collocations(short sequences of adjacent words) in topic models have either relied on a pipeline approach, restricted attention to bigrams,or resulted in models whose inference does not scale to large corpora. This paper studies how to simultaneously learn both collocations and their topic assignments.We present an efficient reformulation of the Adaptor Grammar-based topical collocation model (AG-colloc) (Johnson,2010), and develop a point-wise sampling algorithm for posterior inference in this new formulation. We further improve the efficiency of the sampling algorithm by exploiting sparsity and parallelising inference.Experimental results derived in text classification, information retrieval and human evaluation tasks across a range of data sets show that this reformulation scales to hundreds of thousands of documents while maintaining the good performance of the AG-colloc model.
Original languageEnglish
Title of host publicationProceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing
PublisherAssociation for Computational Linguistics
Pages1460-1469
Number of pages10
ISBN (Print)978-1-941643-72-3
Publication statusPublished - 2015

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