Multi-level mining and visualization of scientific text collections

Accuosto, Pablo; Ronzano, Francesco; Ferrés, Daniel; Saggion, Horacio

doi:http://dx.doi.org/10.1145/3127526.3127529

Multi-level mining and visualization of scientific text collections

Citation

Accuosto P, Ronzano F, Ferrés D, Saggion H. Multi-level mining and visualization of scientific text collections. In: WOSP 2017 Proceedings of the 6th International Workshop on Mining Scientific Publications; 2017 Jun 19; Toronto, Canada. New York: ACM; 2017. p. 9-16 DOI: 10.1145/3127526.3127529

Abstract

We present a system to mine and visualize collections of scientific documents by semantically browsing information extracted from single publications or aggregated throughout corpora of articles. The text mining tool performs deep analysis of document collections allowing the extraction and interpretation of research paper’s contents. In addition to the extraction and enrichment of documents with metadata (titles, authors, affiliations, etc), the deep analysis performed comprises semantic interpretation, rhetorical analysis of sentences, triple-based information extraction, and text summarization. The visualization components allow geographicalbased exploration of collections, topic-evolution interpretation, and collaborative network analysis among others. The paper presents a case study of a bilingual collection in the field of Natural Language Processing (NLP).