.png){kind=icon}
.png){kind=icon}
Return
Graph-ranking collective Chinese entity linking algorithm
.png){kind=icon}
PRE
AI
DOI：10.1007/s11704-018-7175-0.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Entity linking (EL) systems aim to link entity mentions in the document to their corresponding entity records in a reference knowledge base. Existing EL approaches usually ignore the semantic correlation between the mentions in the text, and are limited to the scale of the local knowledge base. In this paper, we propose a novel graphranking collective Chinese entity linking (GRCCEL) algorithm, which can take advantage of both the structured relationship between entities in the local knowledge base and the additional background information offered by external knowledge sources. By improved weighted word2vec textual similarity and improved PageRank algorithm, more semantic information and structural information can be captured in the document. With an incremental evidence mining process, more powerful discrimination capability for similar entities can be obtained. We evaluate the performance of our algorithm on some open domain corpus. Experimental results show the effectiveness of our method in Chinese entity linking task and demonstrate the superiority of our method over state-of-the-art methods.
Keywords:
collective entity linking
knowledge mapping
word embedding
entity correlation graph
PageRank
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.6
Papers:
1.6K
Citations:
2.8K