Interactive Text Graph Mining with a Prolog-based Dialog Engine

Paul Tarau, Eduardo Blanco

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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, integrate sentence identifiers as graph nodes and after ranking the graph we take advantage of the implicit semantic information that dependency links 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.

Original languageEnglish (US)
Title of host publicationPractical Aspects of Declarative Languages - 22nd International Symposium, PADL 2020, Proceedings
EditorsEkaterina Komendantskaya, Yanhong Annie Liu
PublisherSpringer
Pages3-19
Number of pages17
ISBN (Print)9783030391966
DOIs
StatePublished - 2020
Externally publishedYes
Event22nd International Symposium on Practical Aspects of Declarative Languages, PADL 2020 - New Orleans, United States
Duration: Jan 20 2020Jan 21 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12007 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Symposium on Practical Aspects of Declarative Languages, PADL 2020
Country/TerritoryUnited States
CityNew Orleans
Period1/20/201/21/20

Keywords

  • Dependency graphs
  • Graph-based natural language processing
  • Logic-based dialog engine
  • Synergies between neural and symbolic text processing
  • query-driven salient sentence extraction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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