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While traditional work on text clustering has largely focused on grouping docu- ments by topic, it is conceivable that a user may want to cluster documents along other dimensions, such …...

Dirichlet process mixture model (DPM- M) has great potential for detecting the underlying structure of data. Extensive studies have applied it for text cluster- ing in terms of topics. However, …...

AbstractRecent years have witnessed a surge of interests of using neural topic models for automatic topic extraction from text, since they avoid the complicated mathematical derivations for model inference as …...

AbstractWithin this work we describe a framework for the collection and summarization of information from the Web in an entity-driven manner. The framework consists of a set of appropriate workflows …...

AbstractFine-tuning with pre-trained language models (e.g. BERT) has achieved great success in many language understanding tasks in supervised settings (e.g. text classification). However, relatively little work has been focused on …...

AbstractExtracting relevant user behaviors through customer’s transaction description is one of the ways to collect customer information. In the current text mining field, most of the researches are mainly study …...

AbstractPretrained language models (PLMs) are trained on massive corpora, but often need to specialize to specific domains. A parameter-efficient adaptation method suggests training an adapter for each domain on the …...