La deteción de mensajes salientes de papers sobre investigación en ciencias sociales y su aplicación en búsqueda de documentos

natural language processing provides effective tools to help researchers cope with the growing body of scientific literature. one of the most successful and well-established applications is information extraction, i.e. the extraction of named entities and facts. this application, however, is not well suited to social sciences, since the main messages of the publications are not facts, but rather arguments. in this article we propose a natural language processing methodology in order to detect sentences that convey salient messages in social science research papers. we consider two sentence types that bear salient messages: sentences that sum up the entire article or parts of the article and sentences that convey research issues. such sentences are detected using a dependency parser and special "concept-matching" rules. in a proof-of-concept experiment we have shown the effectiveness of our proposition: searching for articles in the educational science document base built by the eerqi project we have found that the presence of the query word(s) in the salient sentences detected by our tool is an important indicator of the relevance of the article. we have compared the relevance of the articles retrieved with our method with those retrieved by the lucene search engine as configured for the eerqi content base with the default relevance ranking which is based on word frequency measures. the results are complementary, which points to the utility of the integration of our tool into lucene.