Dialogue act tagging with Transformation-Based Learning

Samuel, Ken; Carberry, Sandra; Vijay‐Shanker, K.

doi:10.3115/980691.980757

Cited by 54 publications

(50 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, we tested the speech act analysis model and the discourse analysis model. Samuel (1998) and Reithinger (1997) because test data used in those works consists of English dialogues while we use Korean dialogues. Furthermore the speech acts used in the experiments are different.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Analysis system of speech acts and discourse structures using maximum entropy model

Choi

Cho

Sea

1999

Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics -

View full text Add to dashboard Cite

We propose a statistical dialogue analysis model to determine discourse structures as well as speech acts using maximum entropy model. The model can automatically acquire probabilistic discourse knowledge from a discourse tagged corpus to resolve ambiguities. We propose the idea of tagging discourse segment boundaries to represent the structural information of discourse. Using this representation we can effectively combine speech act analysis and discourse structure analysis in one framework.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Analysis system of speech acts and discourse structures using maximum entropy model

Choi

Cho

Sea

1999

Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics -

View full text Add to dashboard Cite

show abstract

“…We are using an extension of an existing set of dialogue acts (DAMSL) (Core & Allen, 1997), to include the set of actions analysts can perform on data items, which we intend to interpret as implicit communication about the data. Automatic annotation of dialogue acts can be performed at a relatively high level using straightforward statistical classification techniques (Samuel, Carberry & Vijay-Shanker, 1998;Stolcke, Ries, Coccaro, Shriberg, Bates, Jurafsky, Taylor, Martin, Ess-Dykema & Meteer, 2000;Webb, Hepple & Wilks, 2005). From this example, we can see that the analyst browsed the first document (3), but did not copy any information.…”

Section: Adaptive Modelling Of Interactions Using Query Logsmentioning

confidence: 99%

Query Log Analysis for Adaptive Dialogue-Driven Search

Kruschwitz

Webb

Sutcliffe

2009

Handbook of Research on Web Log Analysis

View full text Add to dashboard Cite

The theme of this chapter is the improvement of Information Retrieval and Question Answering systems by the analysis of query logs. Two case studies are discussed. The first describes an intranet search engine working on a university campus which can present sophisticated query modifications to the user. It does this via a hierarchical domain model built using multi-word term co-occurrence data. The usage log was analysed using mutual information scores between a query and its refinement, between a query and its replacement, and between two queries occurring in the same session. The results can be used to validate refinements in the domain model, and to suggest replacements such as domain-dependent spelling corrections. The second case study describes a dialogue-based question answering system working over a closed document collection largely derived from the Web. Logs here are based around explicit sessions in which an analyst interacts with the system. Analysis of the logs has shown that certain types of interaction lead to increased precision of the results. Future versions of the system will encourage these forms of interaction. The conclusions of this chapter are firstly that there is a growing literature on query log analysis, much of it reviewed here, secondly that logs provide many forms of useful information for improving a system, and thirdly that mutual information measures taken with automatic term recognition algorithms and hierarchy construction techniques comprise one approach for enhancing system performance.

show abstract

“…Thus, we can approximate each utterance by the corresponding speech act in the sentential probability P(U i |G i ) as shown in Eq. (14).…”

Section: Statistical Model For Discourse Structure Analysismentioning

confidence: 99%

An Integrated Dialogue Analysis Model for Determining Speech Acts and Discourse Structures

Choi¹,

Kim²,

Seo³

2005

IEICE Transactions on Information and Systems

View full text Add to dashboard Cite

Won Seug CHOI †a) , Harksoo KIM †b) , and Jungyun SEO † †c) , Members SUMMARY Analysis of speech acts and discourse structures is essential to a dialogue understanding system because speech acts and discourse structures are closely tied with the speaker's intention. However, it has been difficult to infer a speech act and a discourse structure from a surface utterance because they highly depend on the context of the utterance. We propose a statistical dialogue analysis model to determine discourse structures as well as speech acts using a maximum entropy model. The model can automatically acquire probabilistic discourse knowledge from an annotated dialogue corpus. Moreover, the model can analyze speech acts and discourse structures in one framework. In the experiment, the model showed better performance than other previous works.

show abstract

Dialogue act tagging with Transformation-Based Learning

Cited by 54 publications

References 8 publications

Analysis system of speech acts and discourse structures using maximum entropy model

Analysis system of speech acts and discourse structures using maximum entropy model

Query Log Analysis for Adaptive Dialogue-Driven Search

An Integrated Dialogue Analysis Model for Determining Speech Acts and Discourse Structures

Contact Info

Product

Resources

About