Improving Context and Category Matching for Entity Search
Chen, Yueguo (Renmin University of China) | Gao, Lexi (Renmin University of China) | Shi, Shuming (Microsoft Research Asia) | Du, Xiaoyong (Renmin University of China) | Wen, Ji-Rong (Renmin University of China)
Entity search is to retrieve a ranked list of named entities of target types to a given query. In this paper, we propose an approach of entity search by formalizing both context matching and category matching. In addition, we propose a result re-ranking strategy that can be easily adapted to achieve a hybrid of two context matching strategies. Experiments on the INEX 2009 entity ranking task show that the proposed approach achieves a significant improvement of the entity search performance (xinfAP from 0.27 to 0.39) over the existing solutions.
Jul-14-2014
- Country:
- Asia > China (0.05)
- North America > United States
- Pennsylvania > Allegheny County > Pittsburgh (0.04)
- Genre:
- Research Report > New Finding (0.46)
- Technology:
- Information Technology
- Artificial Intelligence
- Machine Learning (1.00)
- Natural Language > Text Processing (0.88)
- Representation & Reasoning (1.00)
- Data Science (0.93)
- Information Management (1.00)
- Artificial Intelligence
- Information Technology