Improving Context and Category Matching for Entity Search
Abstract
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.
Cite
Text
Chen et al. "Improving Context and Category Matching for Entity Search." AAAI Conference on Artificial Intelligence, 2014. doi:10.1609/AAAI.V28I1.8711Markdown
[Chen et al. "Improving Context and Category Matching for Entity Search." AAAI Conference on Artificial Intelligence, 2014.](https://mlanthology.org/aaai/2014/chen2014aaai-improving/) doi:10.1609/AAAI.V28I1.8711BibTeX
@inproceedings{chen2014aaai-improving,
title = {{Improving Context and Category Matching for Entity Search}},
author = {Chen, Yueguo and Gao, Lexi and Shi, Shuming and Du, Xiaoyong and Wen, Ji-Rong},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2014},
pages = {16-22},
doi = {10.1609/AAAI.V28I1.8711},
url = {https://mlanthology.org/aaai/2014/chen2014aaai-improving/}
}