Abstract Web search is among the most frequent online activities. In this context, widespread informational queries entail user intentions to obtain knowledge with respect a particular topic or domain. To serve learning needs better, recent research in field of interactive information retrieval has advocated importance moving beyond relevance ranking results and considering user’s state within ...

Rule-based stemmers such as the Porter stemmer are frequently used to preprocess English corpora for topic modeling. In this work, we train and evaluate topic models on a variety of corpora using several different stemming algorithms. We examine several different quantitative measures of the resulting models, including likelihood, coherence, model stability, and entropy. Despite their frequent ...

In this paper, we investigate the challenging task of understanding short text (STU task) by jointly considering topic modeling and knowledge incorporation. Knowledge incorporation can solve the content sparsity problem effectively for topic modeling. Specifically, the phrase topic model is proposed to leverage the auto-mined knowledge, i.e., the phrases, to guide the generative process of shor...

The paper aims to discuss strengths and weaknesses of using Object Role Modeling (ORM) and UML Class Diagrams for conceptual modeling of Topic Maps. Established evaluation criteria for conceptual modeling languages are used to compare Topic Map ontology modeling with ORM and UML, to try to find if ORM is a good alternative to UML. The paper discusses a few extensions to simplify viewing ORM dia...

Induced by “big data,” “topic modeling” has become an attractive alternative to mapping cowords in terms of co-occurrences and co-absences using network techniques. Does topic modeling provide an alternative for co-word mapping in research practices using moderately sized document collections? We return to the word/document matrix using first a single text with a strong argument (“The Leiden Ma...

The importance of unsupervised clustering and topic modeling is well recognized with everincreasing volumes of text data. In this paper, we propose a fast method for hierarchical clustering and topic modeling called HierNMF2. Our method is based on fast Rank-2 nonnegative matrix factorization (NMF) that performs binary clustering and an efficient node splitting rule. Further utilizing the final...