Interactive Text Graph Mining with a Prolog-Based Dialog Engine

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 ele...

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Veröffentlicht in:Theory and practice of logic programming Jg. 21; H. 2; S. 244 - 263
Hauptverfasser: TARAU, PAUL, BLANCO, EDUARDO
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cambridge Cambridge University Press 01.03.2021
Schlagworte:
Algorithms
Language
Logic programming
Natural language processing
Neural networks
Prolog
Queries
Source code
ISSN:1471-0684, 1475-3081
Online-Zugang:Volltext
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Zusammenfassung: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 .
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:1471-0684
1475-3081
DOI:10.1017/S1471068420000137