Annotated Corpus for Citation Context Analysis

• Main Authors: Myriam Hernández-Álvarez, José Gómez Soriano, Patricio Martínez-Barco
• Format: Article
• Language: English
• Published: Escuela Politécnica Nacional (EPN) 2016-05-01
• Series: Latin-American Journal of Computing
• Subjects: Corpus; annotation; methodology; machine- learning; function; polarity; aspects; schema; keywords; labels; classification
• Online Access: http://lajc.epn.edu.ec/index.php/LAJC/article/view/102
• ISSN: 1390-9266; 1390-9134

In this paper, we present a corpus composed of 85 scientific articles annotated with 2092 citations analyzed using context analysis. We obtained a high Inter-annotator agreement; therefore, we assure reliability and reproducibility of the annotation performed by three coders in an independent way. We applied this corpus to classify citations according to qualitative criteria using a medium granularity categorization scheme enriched by annotated keywords and labels to obtain high granularity. The annotation schema handle three dimensions: PURPOSE: POLARITY: ASPECTS. Citation purpose define functions classification: use, critique, comparison and background with more specific classes stablished using keywords: Based on, Supply; Useful; Contrast; Acknowledge, Corroboration, Debate; Weakness and Hedges. Citation aspects complement the citation characterization: concept, method, data, tool, task, among others. Polarity has three levels: Positive, Negative and Neutral. We developed the schema and annotated the corpus focusing in applications for citation influence assessment, but we suggest that applications as summary generation and information retrieval also could use this annotated corpus because of the organization of the scheme in clearly defined general dimensions.