Conversation Trees: A Grammar Model for Topic Structure in Forums

34483

Authors

nan

Abstract

Online forum discussions proceed differ- ently from face-to-face conversations and any single thread on an online forum con- tains posts on different subtopics. This work aims to characterize the content of a forum thread as a conversation tree of topics. We present models that jointly per- form two tasks: segment a thread into sub- parts, and assign a topic to each part. Our core idea is a definition of topic struc- ture using probabilistic grammars. By leveraging the flexibility of two grammar formalisms, Context-Free Grammars and Linear Context-Free Rewriting Systems, our models create desirable structures for forum threads: our topic segmentation is hierarchical, links non-adjacent segments on the same topic, and jointly labels the topic during segmentation. We show that our models outperform a number of tree generation baselines. 1

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Full Content

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1543–1553, Lisbon, Portugal, 17-21 September 2015. c⃝2015 Association for Computational Linguistics. Conversation Trees: A Grammar Model for Topic Structure in Forums Annie Louis and Shay B. Cohen School of Informatics University of Edinburgh Edinburgh, EH8 9AB, UK {alouis,scohen}@inf.ed.ac.uk Abstract Online forum discussions proceed differ- ently from face-to-face conversations and any single thread on an online forum con- tains posts on different subtopics. This work aims to characterize the content of a forum thread as a conversation tree of topics. We present models that jointly per- form two tasks: segment a thread into sub- parts, and assign a topic to each part. Our core idea is a definition of topic struc- ture using probabilistic grammars. By leveraging the flexibility of two grammar formalisms, Context-Free Grammars and Linear Context-Free Rewriting Systems, our models create desirable structures for forum threads: our topic segmentation is hierarchical, links non-adjacent segments on the same topic, and jointly labels the topic during segmentation. We show that our models outperform a number of tree generation baselines. 1 Introduction Online forums are commonplace today and used for various purposes: product support and trou- bleshooting, opining about events and people, and student interaction on online course platforms. Threads in these forums become long, involve posts from multiple users, and the chronological order of the posts in a thread does not represent a continuous flow of dialog. Adding structure to these threads is important for tasks such as infor- mation extraction, search, and summarization. One such aspect of structure is topic. Figure 1 shows a computer-troubleshooting related thread with six posts. The first post is the troubleshoot- ing question and the remaining posts can be seen as focusing on either of two topics, the driver soft- ware (posts p1, p2, p5) or the speaker hardware p0 Bob: When I play a recorded video on my camera, it looks and sounds fine. On my computer, it plays at a really fast rate and sounds like Alvin and the Chipmunks! p1 Kate: I’d find and install the latest audio driver. p2 Mary: The motherboard supplies the clocks for audio feedback. So update the audio and motherboard drivers. p3 Chris: Another fine mess in audio is volume and speaker settings. You checked these? p4 Jane: Yes, under speaker settings, look for hardware ac- celeration. Turning it off worked for me. p5 Matt: Audio drivers are at this link. Rather than just audio drivers, I would also just do all drivers. Table 1: Example forum thread conversation (p3, p4). By categorizing posts into such topics, we can provide a useful division of content in a thread and even across multiple threads. Note that the driver topic is not a contiguous sequence but present in non-adjacent parts, (p1, p2) and (p5). We tackle the problem of joint topic segmenta- tion and topic labeling of forum threads. Given a thread’s posts in chronological order (the order in which they were posted), we create a …

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Labeled Text

Online forum discussions proceed differently from face-to-face conversations and any single thread on an online forum contains posts on different subtopics. This work aims to characterize the content of a forum thread as a conversation tree of topics. We present models that jointly perform two tasks: segment a thread into subparts, and assign a topic to each part. Our core idea is a definition of topic structure using probabilistic grammars. By leveraging the flexibility of two grammar formalisms, Context-Free Grammars and Linear Context-Free Rewriting Systems, our models create desirable structures for forum threads: our topic segmentation is hierarchical, links non-adjacent segments on the same topic, and jointly labels the topic during segmentation. We show that our models outperform a number of tree generation baselines. 1

Publication Details

Paper ID:
34483
Year:
2015
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan