PDF] Named entity disambiguation by leveraging wikipedia semantic

A novel similarity measure is proposed to leverage Wikipedia semantic knowledge for disambiguation, which surpasses other knowledge bases by the coverage of concepts, rich semantic information and up-to-date content and has been tested on the standard WePS data sets. Name ambiguity problem has raised an urgent demand for efficient, high-quality named entity disambiguation methods. The key problem of named entity disambiguation is to measure the similarity between occurrences of names. The traditional methods measure the similarity using the bag of words (BOW) model. The BOW, however, ignores all the semantic relations such as social relatedness between named entities, associative relatedness between concepts, polysemy and synonymy between key terms. So the BOW cannot reflect the actual similarity. Some research has investigated social networks as background knowledge for disambiguation. Social networks, however, can only capture the social relatedness between named entities, and often suffer the limited coverage problem. To overcome the previous methods' deficiencies, this paper proposes to use Wikipedia as the background knowledge for disambiguation, which surpasses other knowledge bases by the coverage of concepts, rich semantic information and up-to-date content. By leveraging Wikipedia's semantic knowledge like social relatedness between named entities and associative relatedness between concepts, we can measure the similarity between occurrences of names more accurately. In particular, we construct a large-scale semantic network from Wikipedia, in order that the semantic knowledge can be used efficiently and effectively. Based on the constructed semantic network, a novel similarity measure is proposed to leverage Wikipedia semantic knowledge for disambiguation. The proposed method has been tested on the standard WePS data sets. Empirical results show that the disambiguation performance of our method gets 10.7% improvement over the traditional BOW based methods and 16.7% improvement over the traditional social network based methods.

PDF) Named Entity Disambiguation for Noisy Text

Improving Named Entity Disambiguation using Entity Relatedness

Improving Named Entity Disambiguation using Entity Relatedness

Using Graph Embeddings for Fast Named Entity Disambiguation

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PDF] Named entity disambiguation by leveraging wikipedia semantic

PDF) Named Entity Disambiguation for Noisy Text

Neural entity linking: A survey of models based on deep

Generation of training data for named entity recognition of