@article{
 title = {Short and Sparse Text Topic Modeling via Self-Aggregation},
 type = {article},
 keywords = {Technical Papers — Web and Knowledge-Based Information Systems},
 websites = {http://www.ntu.edu.sg/home/sinnopan/publications/[IJCAI15]Short%20and%20Sparse%20Text%20Topic%20Modeling%20via%20Self-Aggregation.pdf},
 id = {25622032-7e08-3452-8748-d62a736c6c34},
 created = {2018-02-05T18:52:06.015Z},
 accessed = {2018-02-05},
 file_attached = {true},
 profile_id = {371589bb-c770-37ff-8193-93c6f25ffeb1},
 group_id = {f982cd63-7ceb-3aa2-ac7e-a953963d6716},
 last_modified = {2018-02-05T18:52:09.145Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {The overwhelming amount of short text data on social media and elsewhere has posed great chal-lenges to topic modeling due to the sparsity prob-lem. Most existing attempts to alleviate this prob-lem resort to heuristic strategies to aggregate short texts into pseudo-documents before the application of standard topic modeling. Although such strate-gies 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 pa-per, we present a novel model towards this goal by integrating topic modeling with short text aggre-gation during topic inference. The aggregation is founded on general topical affinity of texts rather than particular heuristics, making the model read-ily applicable to various short texts. Experimental results on real-world datasets validate the effective-ness of this new model, suggesting that it can distill more meaningful topics from short texts.},
 bibtype = {article},
 author = {Quan, Xiaojun and Kit, Chunyu and Ge, Yong and Pan, Sinno Jialin}
}

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