Short and Sparse Text Topic Modeling via Self-Aggregation

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015)
EditorsQiang Yang, Michael Wooldridge
Place of PublicationPalo Alto, California USA
PublisherAAAI Press/International Joint Conferences on Artificial Intelligence
Pages2270-2276
ISBN (Print)9781577357384
Publication statusPublished - Jul 2015

Publication series

NameInternational Joint Conference on Artificial Intelligence (IJCAI)
ISSN (Print)1045-0823

Conference

Title24th International Joint Conference on Artificial Intelligence, IJCAI 2015
PlaceArgentina
CityBuenos Aires
Period25 - 31 July 2015

Abstract

The overwhelming amount of short text data on social media and elsewhere has posed great challenges to topic modeling due to the sparsity problem. Most existing attempts to alleviate this problem resort to heuristic strategies to aggregate short texts into pseudo-documents before the application of standard topic modeling. Although such strategies cannot be well generalized to more general genres of short texts, the success has shed light on how to develop a generalized solution. In this paper, we present a novel model towards this goal by integrating topic modeling with short text aggregation during topic inference. The aggregation is founded on general topical affinity of texts rather than particular heuristics, making the model readily applicable to various short texts. Experimental results on real-world datasets validate the effectiveness of this new model, suggesting that it can distill more meaningful topics from short texts.

Citation Format(s)

Short and Sparse Text Topic Modeling via Self-Aggregation. / Quan, Xiaojun; Kit, Chunyu; Ge, Yong; Pan, Sinno Jialin.

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015). ed. / Qiang Yang; Michael Wooldridge. Palo Alto, California USA : AAAI Press/International Joint Conferences on Artificial Intelligence, 2015. p. 2270-2276 (International Joint Conference on Artificial Intelligence (IJCAI)).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review