In this paper, we improve previous knowledge-based topic models by proposing a new probabilistic method, called Word Embedding LDA (WE-LDA), which combines ...
A novel knowledge mining method for topic modeling is proposed. A new topic model which could handle the knowledge encoded by word embeddings.
This paper develops a novel topic model—called Mixed Word Correlation Knowledge-based Latent Dirichlet Allocation—to infer latent topics from text corpus ...
Oct 22, 2024 · Word embeddings, on the other hand, can automatically capture both semantic and syntactic information of words from a large amount of documents, ...
Topic modeling has been widely used to mine topics from documents. However, a key weakness of topic modeling is that it needs a large amount of data.
"Mining Coherent Topics in Documents using Word Embeddings and Large-scale Text Data." Engineering Applications of Artificial Intelligence 64 (2017): 432 ...
This research exemplifies how statistical semantic models and word embedding techniques can play a role in understanding the system of human knowledge.
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Mining coherent topics in documents using word embeddings and large-scale text data. 0.47. 2017, Comparative study of word embedding methods in topic ...
Mining coherent topics in documents using word embeddings and large-scale text data · A novel topic model for documents by incorporating semantic relations ...
Jan 23, 2022 · An extensive study with eight neural-topic models has been completed to check the effectiveness of additional fine-tuning and pretrained word embedding in ...