AKEM: Aligning Knowledge Base to Queries with Ensemble Model for Entity Recognition and Linking

Di Lu, Zhongping Liang, Caixia Yuan, Xiaojie Wang
9/12/2023
cs.CLcs.IR

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Abstract

This paper presents a novel approach to address the Entity Recognition and Linking Challenge at NLPCC 2015. The task involves extracting named entity mentions from short search queries and linking them to entities within a reference Chinese knowledge base. To tackle this problem, we first expand the existing knowledge base and utilize external knowledge to identify candidate entities, thereby improving the recall rate. Next, we extract features from the candidate entities and utilize Support Vector Regression and Multiple Additive Regression Tree as scoring functions to filter the results. Additionally, we apply rules to further refine the results and enhance precision. Our method is computationally efficient and achieves an F1 score of 0.535.

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Cite this paper

@article{lu2023akem,
  title  = {AKEM: Aligning Knowledge Base to Queries with Ensemble Model for Entity Recognition and Linking},
  author = {Di Lu and Zhongping Liang and Caixia Yuan and Xiaojie Wang},
  year   = {2023},
  eprint = {2309.06175},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.06175v2}
}

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