Discovering and disambiguating named entities in text
Discovering and disambiguating named entities in text
复制标题
发现文本中的命名实体并消除歧义
DOI：
10.1145/2483574.2483582
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Johannes Hoffart
中科院分区：
文献类型：
--
作者：
Johannes Hoffart
消除自然语言文本中命名实体的歧义将歧义名称映射到在知识库（如DBpedia、Freebase或YAGO）中注册的规范实体。了解特定实体是其他几项任务的重要资产，例如基于实体的信息检索或更高级别的信息提取。我们的方法命名实体消歧利用几个成分：被提到的实体的先验概率，在文本中提到的上下文和实体之间的相似性，以及实体之间的连贯性。扩展该方法，我们提出了一种新的和高效的措施来计算实体之间的语义一致性。这种度量对于长尾实体或尚未出现在知识库中的实体特别强大。可靠地识别输入文本中不属于知识库的名称是我们目前工作的重点。
Disambiguating named entities in natural language texts maps ambiguous names to canonical entities registered in a knowledge base such as DBpedia, Freebase, or YAGO. Knowing the specific entity is an important asset for several other tasks, e.g. entity-based information retrieval or higher-level information extraction. Our approach to named entity disambiguation makes use of several ingredients: the prior probability of an entity being mentioned, the similarity between the context of the mention in the text and an entity, as well as the coherence among the entities. Extending this method, we present a novel and highly efficient measure to compute the semantic coherence between entities. This measure is especially powerful for long-tail entities or such entities that are not yet present in the knowledge base. Reliably identifying names in the input text that are not part of the knowledge base is the current focus of our work.
影响因子：
14.4
作者：
Hoffart, Johannes;Suchanek, Fabian M.;Weikum, Gerhard
通讯作者：
Weikum, Gerhard