Learning hidden topics from data streams has become absolutely necessary but posed challenging problems such as concept drift well short and noisy data. Using prior knowledge to enrich a topic model is one of potential solutions cope with these challenges. Prior that derived human (e.g. Wordnet) or pre-trained Word2vec) very valuable useful help models work better. However, in streaming environ...

The purpose of this paper is to show how the basic Topic-Comment ordering pattern Hakka can be accounted for by constraint-based optimality theory. Part linguistic data used in adopted from Xu (2002), while those examples presented syntactic tests are created author. These sentences have been further checked and confirmed a native speaker Hakka. This proposes an Optimality Theoretic (OT) model ...

Topic extraction and evolution analysis became a research hotspot in the academic community due to its ability reveal development trend of certain field discover law topic content different stages field. However, current methods still face challenges, such as inaccurate recognition unclear paths, which can seriously compromise comprehensiveness accuracy analysis. To address problem, paper propo...

Documents are usually represented in the bag-of-word space. However, this representation does not take into account the possible relations between words. We propose here a graphical model for representing documents: the Theme Topic Mixture Model (TTMM). This model assumes two types of relations among textual data. Topics link words to each other and Themes gather documents with particular distr...