Interactive Text Graph Mining with a Prolog-Based Dialog Engine

Paul Tarau, Eduardo Blanco

Research output: Contribution to journalReview articlepeer-review

2 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 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.

Original languageEnglish (US)
Pages (from-to)244-263
Number of pages20
JournalTheory and Practice of Logic Programming
Volume21
Issue number2
DOIs
StatePublished - Mar 2021
Externally publishedYes

Keywords

  • dependency graphs
  • graph-based natural language processing
  • logic-based dialog engine
  • query-driven salient sentence extraction
  • synergies between neural and symbolic text processing

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics
  • Artificial Intelligence

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