Towards a top-down approach for an automatic discourse analysis for Basque: Segmentation and Central Unit detection tool

• Published in: PloS one Vol. 14; no. 9; p. e0221639
• Main Authors: Atutxa, Aitziber, Bengoetxea, Kepa, Diaz de Ilarraza, Arantza, Iruskieta, Mikel
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 04-09-2019; Public Library of Science (PLoS)
• Subjects: Analyzers; Annotations; Automation; Biology and Life Sciences; Computer and Information Sciences; Data mining; Detection equipment; Discourse analysis; Engineering and Technology; Linguistics; Medical research; Mining industry; Natural Language Processing; Neural networks; Parsers; Qualitative research; Retirement benefits; Segmentation; Sentiment analysis; Social Sciences
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0221639

Lately, discourse structure has received considerable attention due to the benefits its application offers in several NLP tasks such as opinion mining, summarization, question answering, text simplification, among others. When automatically analyzing texts, discourse parsers typically perform two different tasks: i) identification of basic discourse units (text segmentation) ii) linking discourse units by means of discourse relations, building structures such as trees or graphs. The resulting discourse structures are, in general terms, accurate at intra-sentence discourse-level relations, however they fail to capture the correct inter-sentence relations. Detecting the main discourse unit (the Central Unit) is helpful for discourse analyzers (and also for manual annotation) in improving their results in rhetorical labeling. Bearing this in mind, we set out to build the first two steps of a discourse parser following a top-down strategy: i) to find discourse units, ii) to detect the Central Unit. The final step, i.e. assigning rhetorical relations, remains to be worked on in the immediate future. In accordance with this strategy, our paper presents a tool consisting of a discourse segmenter and an automatic Central Unit detector.