AKEM: Aligning Knowledge Base to Queries with Ensemble Model for Entity
Recognition and Linking
- URL: http://arxiv.org/abs/2309.06175v2
- Date: Wed, 13 Sep 2023 03:53:43 GMT
- Title: AKEM: Aligning Knowledge Base to Queries with Ensemble Model for Entity
Recognition and Linking
- Authors: Di Lu and Zhongping Liang and Caixia Yuan and Xiaojie Wang
- Abstract summary: 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.
Our method is computationally efficient and achieves an F1 score of 0.535.
- Score: 15.548722102706867
- License: http://creativecommons.org/licenses/by/4.0/
- 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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