A plan recognition model for subdialogues in conversations

Litman, Diane; Allen, James F.

doi:10.1016/s0364-0213(87)80005-8

Cited by 177 publications

(69 citation statements)

References 22 publications

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“…Mann and Thompson 1987;Nagano 1986;Ichikawa 1978] and AI researchers [e.g. Litman and Allen 1987;Allen 1995;Schank and Riesbeck 1981;Schank and Abelson 1977] and [Hobbs 1979[Hobbs , 1985.…”

Section: A Typology Of Causal Relationsmentioning

confidence: 99%

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Acquiring causal knowledge from text using the connective marker tame

Inui

Matsumoto

2005

ACM Transactions on Asian Language Information Processing

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In this paper, we deal with automatic knowledge acquisition from text, specifically the acquisition of causal relations. A causal relation is the relation existing between two events such that one event causes (or enables) the other event, such as "hard rain causes flooding" or "taking a train requires buying a ticket." In previous work these relations have been classified into several types based on a variety of points of view. In this work, we consider four types of causal relations-cause, effect, precond(ition) and means-mainly based on agents' volitionality, as proposed in the research field of discourse understanding. The idea behind knowledge acquisition is to use resultative connective markers, such as "because," "but," and "if " as linguistic cues. However, there is no guarantee that a given connective marker always signals the same type of causal relation. Therefore, we need to create a computational model that is able to classify samples according to the causal relation. To examine how accurately we can automatically acquire causal knowledge, we attempted an experiment using Japanese newspaper articles, focusing on the resultative connective "tame." By using machine-learning techniques, we achieved 80% recall with over 95% precision for the cause, precond, and means relations, and 30% recall with 90% precision for the effect relation. Furthermore, the classification results suggest that one can expect to acquire over 27,000 instances of causal relations from 1 year of Japanese newspaper articles.

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“…Mann and Thompson 1987;Nagano 1986;Ichikawa 1978] and AI researchers [e.g. Litman and Allen 1987;Allen 1995;Schank and Riesbeck 1981;Schank and Abelson 1977] and [Hobbs 1979[Hobbs , 1985.…”

Section: A Typology Of Causal Relationsmentioning

confidence: 99%

“…In this work, we focus mainly on a set of causal relations related to the Allen's plan operator [Litman and Allen 1987;Allen 1995] which was proposed in the research field of discourse understanding. Figure 1 is a simple example of the plan operator.…”

Section: A Typology Of Causal Relationsmentioning

confidence: 99%

Acquiring causal knowledge from text using the connective marker tame

Inui

Matsumoto

2005

ACM Transactions on Asian Language Information Processing

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“…Plan recognition mechanisms proposed in the literature are based on one of the following techniques: (1) plausible inference Litman & Allen, 1987;Carberry, 1988Carberry, , 1990Raskutti & Zukerman, 1991), (2) parsing (Sidner, 1985;Vilain, 1990), (3) deductive logic (Kautz & Allen, 1986;Hecking, 1988), and (4) abduction (Konolige & Pollack, 1989;Appelt & Pollack, 1992). The ability to reason about plans has been used in widely diverse tasks, such as resolving referring expressions (Grosz, 1977), building a psychological model (Schmidt, Sridharan & Goodson, 1978) and understanding stories (Schank & Abelson, 1977;Wilensky, 1983;Charniak & Goldman, 1993).…”

Section: Related Researchmentioning

confidence: 99%

“…The ability to reason about plans has been used in widely diverse tasks, such as resolving referring expressions (Grosz, 1977), building a psychological model (Schmidt, Sridharan & Goodson, 1978) and understanding stories (Schank & Abelson, 1977;Wilensky, 1983;Charniak & Goldman, 1993). The incorporation of plan recognition capabilities into computerized information providers has enabled a range of cooperative behaviours, such as supplying more information than what is explicitly requested , responding to ill-formed queries (Carberry, 1988), and understanding (a) indirect speech acts (Perrault & Allen, 1980), (b) inter-sentential ellipsis (Carberry, 1985;Litman, 1986), (c) queries based on invalid plans (Pollack, 1990), and (d) sub-dialogues entered into to debug or correct plans (Sidner, 1985;Litman & Allen, 1987). More recently, there has been research to develop plan recognition systems that ar e capable of considering multiple alternatives in order to recognize intentions that are developed and revised over multiple utterances (Kautz & Allen, 1986;Carberry, 1990;Raskutti & Zukerman, 1991;Appelt & Pollack, 1992;Charniak & Goldman, 1993).…”

Section: Related Researchmentioning

confidence: 99%

Generating queries and replies during information-seeking interactions

Raskutti¹,

Zukerman²

1997

International Journal of Human-Computer Studies

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“…RELATED WORKS specific knowledge[1 ] [2] [3][4]. These models depend on the costly handcrafted knowledge so that it is not easy to be scaled up and be expanded into other domains.…”

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confidence: 99%

Speech Act Classification Based on Individual Statistical Models in a Multi-Domain

Kang

Kim

et al. 2007

RO-MAN 2007 - The 16th IEEE International Symposium on Robot and Human Interactive Communication

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Speech act classification is an essential part of a dialogue system because it is very important to catch user's intention. The previous approaches on speech act classification were focused on obtaining high performances in a single-domain, but they did not deal with a feature interference problem that frequently rises in a multi-domain. In this paper, we propose a two-step system for speech act classification in a multi-domain. In a first step, the proposed system detects a dialogue domain associated with user's utterance. In the second step, the proposed system determines the speech act of his/her utterance based on the statistical information of the detected domain. Owing to this architecture, the proposed system showed higher precision of 5.5% than the baseline system based on the mixed statistical information.

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A plan recognition model for subdialogues in conversations

Cited by 177 publications

References 22 publications

Acquiring causal knowledge from text using the connective marker tame

Acquiring causal knowledge from text using the connective marker tame

Generating queries and replies during information-seeking interactions

Speech Act Classification Based on Individual Statistical Models in a Multi-Domain

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