Journal
WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018)
Volume -, Issue -, Pages 1105-1114Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3178876.3186009
Keywords
Topic modeling; short texts; non-negative matrix factorization; word embedding
Funding
- National Science Foundation [IIS-1619028, IIS-1707498, IIS-1646881]
- National Research Foundation of Korea (NRF) - Korean government (MSIP) [NRF-2016R1C1B2015924]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1707498] Funding Source: National Science Foundation
Ask authors/readers for more resources
Being a prevalent form of social communications on the Internet, billions of short texts are generated everyday. Discovering knowledge from them has gained a lot of interest from both industry and academia. The short texts have a limited contextual information, and they are sparse, noisy and ambiguous, and hence, automatically learning topics from them remains an important challenge. To tackle this problem, in this paper, we propose a semantics-assisted non-negative matrix factorization (SeaNMF) model to discover topics for the short texts. It effectively incorporates the word-context semantic correlations into the model, where the semantic relationships between the words and their contexts are learned from the skip-gram view of the corpus. The SeaNMF model is solved using a block coordinate descent algorithm. We also develop a sparse variant of the SeaNMF model which can achieve a better model interpretability. Extensive quantitative evaluations on various real-world short text datasets demonstrate the superior performance of the proposed models over several other state-of-the-art methods in terms of topic coherence and classification accuracy. The qualitative semantic analysis demonstrates the interpretability of our models by discovering meaningful and consistent topics. With a simple formulation and the superior performance, SeaNMF can be an effective standard topic model for short texts.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available