Discourse-Level Dialogue Management

Galitsky, Boris

doi:10.1007/978-3-030-04299-8_11

Cited by 3 publications

(1 citation statement)

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“…The authors believed that a knowledge exploration should be driven by navigating a discourse tree built for the whole corpus of relevant content. They called such a tree as an extended discourse tree (EDT, (Galitsky, 2019)). It is a combination of discourse trees of individual paragraphs first across paragraphs in a document and then across documents.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Discourse Trees in Forensic Linguistics

Galitsky,

Ilvovsky,

Goncharova

2023

Computational Linguistics and Intellectual Technologies”

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We extend the concept of a discourse tree (DT) in the discourse representation of text towards data of various forms and natures. The communicative DT to include speech act theory, extended DT to ascend to the level of multiple documents, entity DT to track how discourse covers various entities were defined previously in computational linguistics, we now proceed to the next level of abstraction and formalize discourse of not only text and textual documents but also various kinds of accompanying data. We call such discourse representation Multimodal Discourse Trees (MMDTs). The rational for that is that the same rhetorical relations that hold between text fragments also hold between data values, sets and records, such as Reason, Cause, Enablement, Contrast, Temporal sequence. MMDTs are evaluated with respect to the accuracy of recognition of criminal cases when both text and data records are available. MMDTs are shown to contribute significantly to the recognition accuracy in cases where just keywords and syntactic signals are insufficient for classification and discourse-level information needs to be involved.

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Section: Introductionmentioning

confidence: 99%