Knowledge Extraction For Information Retrieval
Proceedings of the 13th International Conference on The Semantic Web. Latest Advances and New Domains - Volume 9678(2016)
摘要
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 3.5% points when combining textual and semantic analysis, thus suggesting that semantic content can effectively improve the performances of Information Retrieval systems.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要