Collective Classification of Congressional Floor-Debate Transcripts

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1506–1515, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Collective Classification of Congressional Floor-Debate Transcripts Clinton Burfoot, Steven Bird and Timothy Baldwin Department of Computer Science and Software Engineering University of Melbourne, VIC 3010, Australia {cburfoot, sb, tim}@csse.unimelb.edu.au Abstract This paper explores approaches to sentiment classification of U.S. Congressional floor- debate transcripts. Collective classification techniques are used to take advantage of the informal citation structure present in the de- bates. We use a range of methods based on local and global formulations and introduce novel approaches for incorporating the outputs of machine learners into collective classifica- tion algorithms. Our experimental evaluation shows that the mean-field algorithm obtains the best results for the task, significantly out- performing the benchmark technique. 1 Introduction Supervised document classification is a well-studied task. Research has been performed across many document types with a variety of classification tasks. Examples are topic classification of newswire ar- ticles (Yang and Liu, 1999), sentiment classifica- tion of movie reviews (Pang et al., 2002), and satire classification of news articles (Burfoot and Baldwin, 2009). This and other work has established the use- fulness of document classifiers as stand-alone sys- tems and as components of broader NLP systems. This paper deals with methods relevant to super- vised document classification in domains with net- work structures, where collective classification can yield better performance than approaches that con- sider documents in isolation. Simply put, a network structure is any set of relationships between docu- ments that can be used to assist the document clas- sification process. Web encyclopedias and scholarly publications are two examples of document domains where network structures have been used to assist classification (Gantner and Schmidt-Thieme, 2009; Cao and Gao, 2005). The contribution of this research is in four parts: (1) we introduce an approach that gives better than state of the art performance for collective classifica- tion on the ConVote corpus of congressional debate transcripts (Thomas et al., 2006); (2) we provide a comparative overview of collective document classi- fication techniques to assist researchers in choosing an algorithm for collective document classification tasks; (3) we demonstrate effective novel approaches for incorporating the outputs of SVM classifiers into collective classifiers; and (4) we demonstrate effec- tive novel feature models for iterative local classifi- cation of debate transcript data. In the next section (Section 2) we provide a for- mal definition of collective classification and de- scribe the ConVote corpus that is the basis for our experimental evaluation. Subsequently, we describe and critique the established benchmark approach for congressional floor-debate transcript classification, before describing approaches based on three alterna- tive collective classification algorithms (Section 3). We then present an experimental evaluation (Sec- tion 4). Finally, we describe related work (Section 5) and offer analysis and conclusions (Section 6). 2 Task Definition 2.1 Collective Classification Given a network and an object o in the network, there are three types of correlations that can be used 1506 to …

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