FICLONE: Improving DBpedia Spotlight Using Named Entity Recognition and Collective Disambiguation
作者: Mohamed ChabchoubMichel GagnonAmal Zouaq
刊名: Open Journal of Semantic Web, 2018, Vol.5 (1), pp.12-28
来源数据库: RonPub UG
原始语种摘要: In this paper we present FICLONE, which aims to improve the performance of DBpedia Spotlight, not only for the task of semantic annotation (SA), but also for the sub-task of named entity disambiguation (NED). To achieve this aim, first we enhance the spotting phase by combining a named entity recognition system (Stanford NER ) with the results of DBpedia Spotlight. Second, we improve the disambiguation phase by using coreference resolution and exploiting a lexicon that associates a list of potential entities of Wikipedia to surface forms. Finally, to select the correct entity among the candidates found for one mention, FICLONE relies on collective disambiguation, an approach that has proved successful in many other annotators, and that takes into consideration the other mentions in the...
全文获取路径: RonPub UG出版社 

  • disambiguation 消除二[多]义性
  • Recognition 识别
  • coreference 互指
  • named 命名
  • spotting 去油污渍
  • recognition 识别
  • achieve 达到
  • semantic 语义上的
  • improve 改进
  • select 选择