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Interactive Text Graph Mining with a Prolog-Based Dialog Engine

Published online by Cambridge University Press:  07 October 2020

PAUL TARAU  and
EDUARDO BLANCO Open the ORCID record for EDUARDO BLANCO [Opens in a new window]
PAUL TARAU
Affiliation:
Department of Computer Science and Engineering, University of North Texas, 1155 Union Circle, Denton, Texas76203, USA, (e-mails:paul.tarau@unt.edu, eduardo.blanco@unt.edu)
EDUARDO BLANCO
Affiliation:
Department of Computer Science and Engineering, University of North Texas, 1155 Union Circle, Denton, Texas76203, USA, (e-mails:paul.tarau@unt.edu, eduardo.blanco@unt.edu)
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Abstract

On top of a neural network-based dependency parser and a graph-based natural language processing module, we design a Prolog-based dialog engine that explores interactively a ranked fact database extracted from a text document. We reorganize dependency graphs to focus on the most relevant content elements of a sentence and integrate sentence identifiers as graph nodes. Additionally, after ranking the graph, we take advantage of the implicit semantic information that dependency links and WordNet bring in the form of subject–verb–object, “is-a” and “part-of” relations. Working on the Prolog facts and their inferred consequences, the dialog engine specializes the text graph with respect to a query and reveals interactively the document’s most relevant content elements. The open-source code of the integrated system is available at https://github.com/ptarau/DeepRank.

Keywords

logic-based dialog enginegraph-based natural language processingdependency graphsquery-driven salient sentence extractionsynergies between neural and symbolic text processing
Type
Rapid Communication
Information
Theory and Practice of Logic Programming , Volume 21 , Issue 2 , March 2021 , pp. 244 - 263
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Copyright
© The Author(s), 2020. Published by Cambridge University Press

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Footnotes

*

We are thankful to the anonymous reviewers of PADL’2020 for their careful reading and constructive suggestions.

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