Interactive Text Graph Mining with a Prolog-Based Dialog Engine. Issue 2 (7th March 2021)

• Record Type: Journal Article
• Authors: TARAU, PAUL; BLANCO, EDUARDO
• Is Part Of: Theory and practice of logic programming. Volume 21:Issue 2(2021)
• Page Start: 244
• Page End: 263
• Publication Date: 2021-03-07
• Subjects: logic-based dialog engine -- graph-based natural language processing -- dependency graphs -- query-driven salient sentence extraction -- synergies between neural and symbolic text processing; Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S1471068420000137
• Languages: English
• ISSNs: 1471-0684

Abstract

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 .