Visualizing the Flow of Discourse with a Concept Ontology
Understanding and visualizing human discourse has long being a challenging task. Although recent work on argument mining have shown success in classifying the role of various sentences, the task of recognizing concepts and understanding the ways in which they are discussed remains challenging. Given an email thread or a transcript of a group discussion, our task is to extract the relevant concepts and understand how they are referenced and re-referenced throughout the discussion. In the present work, we present a preliminary approach for extracting and visualizing group discourse by adapting Wikipedia's category hierarchy to be an external concept ontology. From a user study, we found that our method achieved better results than 4 strong alternative approaches, and we illustrate our visualization method based on the extracted discourse flows.
Code (1)
Tasks
Argument MiningSimilar Papers 제목 키워드 기반
The Information Flow Foundation for Conceptual Knowledge Organization
The sharing of ontologies between diverse communities of discourse allows them to compare their own information structures with that of other communities that share a common terminology and semantics - ontology sharing f…
Conceptual Knowledge Markup Language: An Introduction
Conceptual Knowledge Markup Language (CKML) is an application of XML. Earlier versions of CKML followed rather exclusively the philosophy of Conceptual Knowledge Processing (CKP), a principled approach to knowledge repre…
PhilosophyThe ERA of FOLE: Foundation
This paper discusses the representation of ontologies in the first-order logical environment {\ttfamily FOLE}. An ontology defines the primitives with which to model the knowledge resources for a community of discourse. …
AttributeFormGeneral ClassificationTowards Structural Natural Language Formalization: Mapping Discourse to Controlled Natural Language
The author describes a conceptual study towards mapping grounded natural language discourse representation structures to instances of controlled language statements. This can be achieved via a pipeline of preexisting sta…
Discourse-Based Approach to Involvement of Background Knowledge for Question Answering
We introduce a concept of a virtual discourse tree to improve question answering (Q/A) recall for complex, multi-sentence questions. Augmenting the discourse tree of an answer with tree fragments obtained from text corpo…
Question AnsweringSentence