Conference Proceeding Article
Semantic visualization integrates topic modeling and visualization, such that every document is associated with a topic distribution as well as visualization coordinates on a low-dimensional Euclidean space. We address the problem of semantic visualization for short texts. Such documents are increasingly common, including tweets, search snippets, news headlines, or status updates. Due to their short lengths, it is difficult to model semantics as the word co-occurrences in such a corpus are very sparse. Our approach is to incorporate auxiliary information, such as word embeddings from a larger corpus, to supplement the lack of co-occurrences. This requires the development of a novel semantic visualization model that seamlessly integrates visualization coordinates, topic distributions, and word vectors. We propose a model called GaussianSV, which outperforms pipelined baselines that derive topic models and visualization coordinates as disjoint steps, as well as semantic visualization baselines that do not consider word embeddings.
Machine Learning, Data Mining, Feature Selection/Construction, Learning Graphical Models
Databases and Information Systems | Data Storage Systems
Data Science and Engineering
Proceedings of the 26th International Joint Conference on Artificial Intelligence IJCAI-17, Melbourne, Australia, August 19-25
City or Country
LE, Van Minh Tuan and LAUW, Hady W..
Semantic visualization for short texts with word embeddings. (2017). Proceedings of the 26th International Joint Conference on Artificial Intelligence IJCAI-17, Melbourne, Australia, August 19-25. 2074-2080. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3766
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