Multi-level mining and visualization of scientific text collections
| Autor: | Pablo Accuosto, Horacio Saggion, Francesco Ronzano, Daniel Ferrés |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Information retrieval
business.industry Computer science Semantic interpretation Collaborative network computer.software_genre Automatic summarization Field (computer science) Visualization Metadata Information extraction Data visualization ComputingMethodologies_DOCUMENTANDTEXTPROCESSING business computer |
| Zdroj: | WOSP@JCDL |
| DOI: | 10.1145/3127526.3127529 |
| Popis: | 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 geographical-based exploration of collections, topic-evolution interpretation, and collaborative network analysis among others. The paper presents a case study of a bi-lingual collection in the field of Natural Language Processing (NLP). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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