Open Domain Short Text Conceptualization: A Generative + Descriptive Modeling Approach

Song, Yangqiu; Wang, Shusen; Wang, Haixun

Open Domain Short Text Conceptualization: A Generative + Descriptive Modeling Approach

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Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Machine Learning. Pages 3820-3825.

Abstract

Concepts embody the knowledge to facilitate our cognitive processes of learning. Mapping short texts to a large set of open domain concepts has gained many successful applications. In this paper, we unify the existing conceptualization methods from a Bayesian perspective, and discuss the three modeling approaches: descriptive, generative, and discriminative models. Motivated by the discussion of their advantages and shortcomings, we develop a generative + descriptive modeling approach. Our model considers term relatedness in the context, and will result in disambiguated conceptualization. We show the results of short text clustering using a news title data set and a Twitter message data set, and demonstrate the effectiveness of the developed approach compared with the state-of-the-art conceptualization and topic modeling approaches.

Yangqiu Song, Shusen Wang, and Haixun Wang. 2015. Open Domain Short Text Conceptualization: A Generative + Descriptive Modeling Approach. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 3820–3825. https://ijcai.org/proceedings/2015/537

@inproceedings{ijcai2015p537,
  title     = {Open Domain Short Text Conceptualization: A Generative + Descriptive Modeling Approach},
  author    = {Song, Yangqiu and Wang, Shusen and Wang, Haixun},
  booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-15}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {Qiang Yang and Michael Wooldridge},
  pages     = {3820--3825},
  year      = {2015},
  month     = {7},
  note      = {Special Track on Machine Learning},
  url       = {https://ijcai.org/proceedings/2015/537},
}

TY  - CPAPER
TI  - Open Domain Short Text Conceptualization: A Generative + Descriptive Modeling Approach
AU  - Song, Yangqiu
AU  - Wang, Shusen
AU  - Wang, Haixun
T2  - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2015
SP  - 3820
EP  - 3825
UR  - https://ijcai.org/proceedings/2015/537
ER  -