Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2017

Abstract

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.

Keywords

Machine Learning, Data Mining, Feature Selection/Construction, Learning Graphical Models

Discipline

Databases and Information Systems | Data Storage Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 26th International Joint Conference on Artificial Intelligence IJCAI-17, Melbourne, Australia, August 19-25

First Page

2074

Last Page

2080

ISBN

9780999241103

Identifier

10.24963/ijcai.2017/288

Publisher

IJCAI

City or Country

Vienna

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.24963/ijcai.2017/288

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