Word embeddings trained on large corpus are encoded with general semantic and syntactic information of words, and hence they can be leveraged to guide topic modeling for short text collections as supplementary information for sparse co-occurrence patterns.
Dec 18, 2018 · In this paper, we design a novel model for short text topic modeling, referred as Conditional Random Field regularized Topic Model (CRFTM).
Sep 27, 2016 · This paper studies how to incorporate the external word correlation knowledge into short texts to improve the coherence of topic modeling.
A novel model for short text topic modeling, referred as Conditional Random Field regularized Topic Model (CRFTM), which not only develops a generalized ...
Abstract: Short texts have become the prevalent format of information on the Internet. Inferring the topics of this type of messages becomes a critical and ...
Short texts have become the prevalent format of information on the Internet. Inferring the topics of this type of messages becomes a critical and ...
We propose an Embedding-based topic modeling (EmTM) approach that incorporates word embedding and hierarchical clustering to identify significant topics.
In this paper, we design a novel model for short text topic modeling, referred as Conditional Random Field regularized Topic Model (CRFTM).
In this paper, we propose a new model called promotion-BTM, which promotes the probability that similar words based on word embedding belong to the same topic.
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