Context-based message expansion for disentanglement of interleaved text conversations

Wang, Lidan; Oard, Douglas W.

doi:10.3115/1620754.1620783

Cited by 31 publications

(31 citation statements)

References 16 publications

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“…In addition, Elsner and Charniak (2008) and Wang and Oard (2009) studied conversation disentanglement in online chat dialogues. Although their work is similar to our work in that they investigated structures of discourses using various features, they focused only on an online chat corpus.…”

Section: Related Workmentioning

confidence: 97%

Online community search using conversational structures

2011

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Online communities are valuable information sources where knowledge is accumulated by interactions between people. Search services provided by online community sites such as forums are often, however, quite poor. To address this, we investigate retrieval techniques that exploit the hierarchical thread structures in community sites. Since these structures are sometimes not explicit or accurately annotated, we introduce structure discovery techniques that use a variety of features to model relations between posts. We then make use of thread structures in retrieval experiments with two online forums and one email archive. Our results show that using thread structures that have been accurately annotated can lead to significant improvements in retrieval performance compared to strong baselines.

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Section: Related Workmentioning

confidence: 97%

Online community search using conversational structures

2011

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“…Our experiments also show it outperforms the other two approaches in similarity estimation (See Section 4). In addition to message contents, contexts such as temporal and user information were also usually considered in previous studies about conversation disentanglement (Wang and Oard, 2009;Charniak, 2010, 2011). In this paper, we focus on the performance of message content representations and only incorporate four context features: speaker identicality, absolute time difference and the number of duplicated words with and without weighting by inverse document frequency (Christopher et al, 2008).…”

Section: Siamese Hierarchical Cnn (Shcnn)mentioning

confidence: 99%

“…Input : Message set M , the set of selected message pairs D, the threshold of similarity ranks r and the threshold of similarity scores h. Output: A set of conversations C 2 Let G = (M , ;) be an undirected message graph False alarms may be reduced by raising the threshold that determines whether two messages are connected (Wang and Oard, 2009). However, a high threshold can make disentangled conversations fragmented and the best threshold for each pair could vary.…”

Section: Cisir (M D R H);mentioning

confidence: 99%

“…One solution for conversation disentanglement is to model the task as a topic detection and tracking (TDT) (Allan, 2002) task by deciding whether each incoming message starts a new topic or belongs to an existing conversation. Messages in the same conversation may have higher similarity scores (Shen et al, 2006;Mayfield et al, 2012) or similar context messages (Wang and Oard, 2009). However, similarity thresholds for determining new topics vary depending on context.…”

Section: Introductionmentioning

confidence: 99%

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Learning to Disentangle Interleaved Conversational Threads with a Siamese Hierarchical Network and Similarity Ranking

Jiang¹,

Chen²,

Chen³

et al. 2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

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An enormous amount of conversation occurs online every day, such as on chat platforms where multiple conversations may take place concurrently. Interleaved conversations lead to difficulties in not only following discussions but also retrieving relevant information from simultaneous messages. Conversation disentanglement aims to separate intermingled messages into detached conversations.In this paper, we propose to leverage representation learning for conversation disentanglement. A Siamese hierarchical convolutional neural network (SHCNN), which integrates local and more global representations of a message, is first presented to estimate the conversation-level similarity between closely posted messages. With the estimated similarity scores, our algorithm for conversation identification by similarity ranking (CISIR) then derives conversations based on highconfidence message pairs and pairwise redundancy. Experiments were conducted with four publicly available datasets of conversations from Reddit and IRC channels. The experimental results show that our approach significantly outperforms comparative baselines in both pairwise similarity estimation and conversation disentanglement.

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“…Recent work on synchronous conversations has been focusing on disentangling multi-party chats, which have a linear structure. For example, several studies propose models to disentangle multi-party chat (Elsner & Charniak, 2010Wang & Oard, 2009;Mayfield, Adamson, & Rosé, 2012). On the other hand, asynchronous conversations like email and social media services (e.g., Gmail, Twitter) generally organize comments into tree-structured threads using headers.…”

Section: Conversational Structure Extractionmentioning

confidence: 99%

Topic Segmentation and Labeling in Asynchronous Conversations

Joty

Carenini

2013

jair

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Topic segmentation and labeling is often considered a prerequisite for higher-level conversation analysis and has been shown to be useful in many Natural Language Processing (NLP) applications. We present two new corpora of email and blog conversations annotated with topics, and evaluate annotator reliability for the segmentation and labeling tasks in these asynchronous conversations. We propose a complete computational framework for topic segmentation and labeling in asynchronous conversations. Our approach extends state-of-the-art methods by considering a fine-grained structure of an asynchronous conversation, along with other conversational features by applying recent graph-based methods for NLP. For topic segmentation, we propose two novel unsupervised models that exploit the fine-grained conversational structure, and a novel graph-theoretic supervised model that combines lexical, conversational and topic features. For topic labeling, we propose two novel (unsupervised) random walk models that respectively capture conversation specific clues from two different sources: the leading sentences and the fine-grained conversational structure. Empirical evaluation shows that the segmentation and the labeling performed by our best models beat the state-of-the-art, and are highly correlated with human annotations.

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Context-based message expansion for disentanglement of interleaved text conversations

Cited by 31 publications

References 16 publications

Online community search using conversational structures

Online community search using conversational structures

Learning to Disentangle Interleaved Conversational Threads with a Siamese Hierarchical Network and Similarity Ranking

Topic Segmentation and Labeling in Asynchronous Conversations

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