The scientific literature is a rich and challenging data source for research in systems biology, providing numerous interactions between biological entities. Text mining technique...
: The increasing number of digitized texts presently available notably on the Web has developed an acute need in text mining techniques. Clustering systems are used more and more o...
Abdelmalek Amine, Zakaria Elberrichi, Michel Simon...
Automated mining of novel documents or sentences from chronologically ordered documents or sentences is an open challenge in text mining. In this paper, we describe the preprocess...
s text mining techniques over Medline abstracts as a method for accessing both these bodies of evidence in a consistent way. In an example use case, we apply our method to create a...
Bursty features in text streams are very useful in many text mining applications. Most existing studies detect bursty features based purely on term frequency changes without takin...
Wayne Xin Zhao, Jing Jiang, Jing He, Dongdong Shan...
Text mining, though still a nascent industry, has been growing quickly along with the awareness of the importance of unstructured data in business analytics, customer retention an...
It is now almost15 years since the publication of the first paper on text mining in the genomics domain, and decades since the first paper on text mining in the medical domain. En...
Pierre Zweigenbaum, Dina Demner-Fushman, Hong Yu, ...
Background: Within the emerging field of text mining and statistical natural language processing (NLP) applied to biomedical articles, a broad variety of techniques have been deve...
Background: The BioCreative text mining evaluation investigated the application of text mining methods to the task of automatically extracting information from text in biomedical ...
Background: Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We...