4 accepted papers
Hierarchical topic modeling, which can mine implicit semantics in the corpus and automatically construct topic hierarchical relationships, has received considerable attention recently. However, the current hierarchical topic models are mainly based on Euclidean space, which cannot well retain the im…
Hierarchical topic models nowadays tend to capture the relationship between words and topics, often ignoring the role of anchor words that guide text generation. For the first time, we detect and add anchor words to the text generation process in an unsupervised way. Firstly, we adopt a clustering a…
The out-of-vocabulary (OOV) words are difficult to represent while critical to the performance of embedding-based downstream models. Prior OOV word embedding learning methods failed to model complex word formation well. In this paper, we propose a novel graph-based relation mining method, namely GRM…
Hierarchical topic models, which can extract semantically meaningful topics from a textcorpus in an unsupervised manner and automatically organise them into a topic hierarchy, have been widely used to discover the underlying semantic structure of documents. However, the existing models often assume…