Grounding Topic Models with Knowledge Bases
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 1578-1583.
Abstract
Topic models represent latent topics as probability distributions over words which can be hard to interpret due to the lack of grounded semantics. In this paper, we propose a structured topic representation based on an entity taxonomy from a knowledge base. A probabilistic model is developed to infer both hidden topics and entities from text corpora. Each topic is equipped with a random walk over the entity hierarchy to extract semantically grounded and coherent themes. Accurate entity modeling is achieved by leveraging rich textual features from the knowledge base. Experiments show significant superiority of our approach in topic perplexity and key entity identification, indicating potentials of the grounded modeling for semantic extraction and language understanding applications.
@inproceedings{ijcai2016p226,
title = {Grounding Topic Models with Knowledge Bases},
author = {Hu, Zhiting and Luo, Gang and Sachan, Mrinmaya and Xing, Eric P. and Nie, Zaiqing},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {1578--1583},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/226},
}
TY - CPAPER TI - Grounding Topic Models with Knowledge Bases AU - Hu, Zhiting AU - Luo, Gang AU - Sachan, Mrinmaya AU - Xing, Eric P. AU - Nie, Zaiqing T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 1578 EP - 1583 UR - https://ijcai.org/proceedings/2016/226 ER -Copied
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