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- 177 creator alessio-palmero-aprosio.
- 177 creator francesco-corcoglioniti.
- 177 creator marco-rospocher.
- 177 creator mauro-dragoni.
- 177 type InProceedings.
- 177 label "Multi-layered Linked Open Data Enrichment for Information Retrieval".
- 177 sameAs 177.
- 177 abstract "Document retrieval is the task of returning relevant textual resources for a given user query. In this paper, we investigate whether the semantic analysis of the query and the documents, obtained exploiting state-of-the-art Natural Language Processing techniques (e.g., Entity Linking, Frame Detection) and Semantic Web resources (e.g., Yago, DBpedia), can improve the performances of the traditional term-based similarity approach. Our experiments, conducted on a recently released document collection, show that Mean Average Precision (MAP) increases of around 4 percentage points when combining textual and semantic analysis, thus suggesting that semantic content can effectively improve the performances of Information Retrieval systems.".
- 177 hasAuthorList authorList.
- 177 isPartOf proceedings.
- 177 title "Multi-layered Linked Open Data Enrichment for Information Retrieval".