Importance Based Entity Ranking for News Documents

Subjects: Library Science，Information Science >> Library Science submitted time 2023-08-26 Cooperative journals: 《图书情报工作》

Lu Na Zhou Pengcheng Wu Chuan

Abstract： [Purpose/significance] We propose an importance based method for entity ranking. Entities in a particular document show different importance. Many researches focus on documents or entities, such as text categorization and entity linking, while few research pay attention to the importance of entities in documents. This research has significant theoretical and practical value. [Method/process] Given a document which consists of words and entities, our method computes the relative importance of entities in the document, and then ranks these entities based on their importance with respect to the document. We perform experiment on the Sogou News dataset, and use evaluation metrics such as NDCG and inversed pair rate to evaluate the results. [Result/conclusion] Experimental results show that methods based on entity frequency, TF*IDF, distribution entropy and TextRank achieve better performance, while method based on cluster coefficient does not work well. In terms of NDCG, TF*IDF method reaches 95.86%, which is the best result and in terms of the inverse rate, the ensemble method reaches 84.46%, which is the best result.

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