Graph-based text representation is one of the important preprocessing steps in data and text
mining, Natural Language Processing (NLP), and information retrieval approaches. The …

We provide a comprehensive survey of the research literature that applies Information
Extraction techniques in a Semantic Web setting. Works in the intersection of these two …

We propose a graph-based semantic model for representing document content. Our method
relies on the use of a semantic network, namely the DBpedia knowledge base, for acquiring …

Topics generated by topic models are typically represented as list of terms. To reduce the
cognitive overhead of interpreting these topics for end-users, we propose labelling a topic …

Semantic relatedness and disambiguation are fundamental problems for linking text
documents to the Web of Data. There are many approaches dealing with both problems but …

Invasive angiography is the reference standard for coronary artery disease (CAD) diagnosis
but is expensive and associated with certain risks. Machine learning (ML) using clinical and …

Objective To demonstrate how non-negative matrix factorization can be used to learn a
temporal topic model over a large collection of primary care clinical notes, characterizing …

Topic modeling has increasingly attracted interests from researchers. Common methods of
topic modeling usually produce a collection of unlabeled topics where each topic is depicted …

Topic models, which frequently represent topics as multinomial distributions over words,
have been extensively used for discovering latent topics in text corpora. Topic labeling …

Latent topics derived by topic models such as Latent Dirichlet Allocation (LDA) are the result
of hidden thematic structures which provide further insights into the data. The automatic …