Segmenting Conversations by Topic, Initiative, and Style

Ries, Klaus

doi:10.1007/3-540-45637-6_5

Cited by 12 publications

(8 citation statements)

References 20 publications

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“…This "blurriness" of boundaries, combined with the prevalence effects discussed in Section 3.2, also explains the fact that topic annotation efforts which were only concerned with roughly dividing a text into segments (Passonneau and Litman 1993;Carletta et al 1997;Hearst 1997;Reynar 1998;Ries 2002) generally report lower agreement than the studies whose goal is to identify smaller discourse units. When disagreement is mostly concentrated in one class ('boundary' in this case), if the total number of units to annotate remains the same, then expected agreement on this class is lower when a greater proportion of the units to annotate belongs to this class.…”

Section: Segmentation and Topicmentioning

confidence: 99%

Inter-Coder Agreement for Computational Linguistics

Artstein

Poesio

2008

Computational Linguistics

1,085

722

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This article is a survey of methods for measuring agreement among corpus annotators. It exposes the mathematics and underlying assumptions of agreement coefficients, covering Krippendorff's alpha as well as Scott's pi and Cohen's kappa; discusses the use of coefficients in several annotation tasks; and argues that weighted, alpha-like coefficients, traditionally less used than kappa-like measures in computational linguistics, may be more appropriate for many corpus annotation tasks—but that their use makes the interpretation of the value of the coefficient even harder.

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Section: Segmentation and Topicmentioning

confidence: 99%

Inter-Coder Agreement for Computational Linguistics

Artstein

Poesio

2008

Computational Linguistics

1,085

722

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“…(Obin et al, 2010) (Ries et al, 2000). Results from this area have been applied to segmentation of conversation by a combination of topic and style (Ries, 2002).…”

Section: Related Workmentioning

confidence: 99%

Aspectual Properties of Conversational Activities

Passonneau¹,

Guan²,

Yeung³

et al. 2014

Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

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Segmentation of spoken discourse into distinct conversational activities has been applied to broadcast news, meetings, monologs, and two-party dialogs. This paper considers the aspectual properties of discourse segments, meaning how they transpire in time. Classifiers were constructed to distinguish between segment boundaries and non-boundaries, where the sizes of utterance spans to represent data instances were varied, and the locations of segment boundaries relative to these instances. Classifier performance was better for representations that included the end of one discourse segment combined with the beginning of the next. In addition, classification accuracy was better for segments in which speakers accomplish goals with distinctive start and end points.

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“…Current approaches to unsupervised text segmentation consider each document to be segmented in isolation. They work either by using coherence features to identify cohesive segments of text1, 2 or by detecting topic shift indicators3. Certain techniques use keyword repetitions4 or semantic networks5 to detect topic boundaries.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised segmentation of conversational transcripts

Kummamuru¹,

Deepak²,

Roy³

et al. 2009

Statistical Analysis

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Contact centers provide dialog based support to organizations to address various customer related issues. We have observed that the calls received at contact centers mostly follow well defined patterns. Such call flows not only specify how an agent should proceed in a call, handle objections, persuade customers, follow compliance issues, etc but also help to structure the operational process of call handling. Automatically identifying such patterns in terms of distinct segments from a collection of transcripts of conversations would improve productivity of agents as well as track compliance to guidelines. Call transcripts from call centers typically tend to be noisy owing to the noise arising from agent/caller distractions, and errors introduced by the speech recognition engine. Such noise makes classical text segmentation algorithms such as TextTiling, which work on each transcript in isolation, very inappropriate. But such noise effects become statistically insignificant over a corpus of similar calls. In this paper, we propose an algorithm to segment conversational transcripts in an unsupervised way utilizing corpus level information of similar call transcripts. We show that our approach outperforms the classical TextTiling algorithm and also describe ways to improve the segmentation using limited supervision. We discuss various ways of evaluating such an algorithm. We apply the proposed algorithm to a corpus of transcripts of calls from a car reservation call center and evaluate it using various evaluation measures. We apply segmentation to the problem of automatically checking the compliance of agents and show that our segmentation algorithm considerably improves the precision.

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Segmenting Conversations by Topic, Initiative, and Style

Cited by 12 publications

References 20 publications

Inter-Coder Agreement for Computational Linguistics

Inter-Coder Agreement for Computational Linguistics

Aspectual Properties of Conversational Activities

Unsupervised segmentation of conversational transcripts

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