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Interactive Text graph Mining with a Prolog-based Dialog Engine

THEORY AND PRACTICE OF LOGIC PROGRAMMING 2021年第2期21卷 244-263页

作者： Tarau, Paul Blanco, Eduardo Univ North Texas Dept Comp Sci & Engn 1155 Union Circle Denton TX 76203 USA

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://***/ptarau/DeepRank.

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

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Interactive Text graph Mining with a Prolog-based Dialog Engine 22nd

Interactive Text Graph Mining with a Prolog-based Dialog Eng...

引用

22nd International Symposium on Practical Aspects of Declarative languages (PADL)

作者： Tarau, Paul Blanco, Eduardo Univ North Texas Dept Comp Sci & Engn Denton TX 76203 USA

ISBN: (纸本)9783030391973;9783030391966

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://***/ptarau/DeepRank.

关键词： Logic-based dialog engine graph-based natural language processing Dependency graphs query-driven salient sentence extraction Synergies between neural and symbolic text processing

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