This paper presents the adaptation of the Hidden Markov Models-based TTL partof-speech tagger to the biomedical domain. TTL is a text processing platform that performs sentence splitting, tokenization, POS tagging, chunking and Named Entity Recognition (NER) for a number of languages, including Romanian. The POS tagging accuracy obtained by the TTL POS tagger exceeds 97% when TTL’s baseline mod...

Digital Signal Processing techniques constitute the basic scientific approach used in most of the current advances in medicine. In particular, the development of algorithms in order to extract, predict and model raw biomedical data series has revolutionized many routine, but data-intensive, areas of current medical practice. In this contribution, we present an evolutionary technique for modelli...

Background: The excellent biocompatibility, biodegradability and biological properties of the hydrogels, fabricated using natural polymers, especially polysaccharides, are very advantageous for biomedical applications. Gum tragacanth (GT) is a heterogeneous highly branched anionic polysaccharide, which has been used extensively in food and pharmaceutical industries. Despite,  its desirable prop...

In this paper, we explore how to adapt a general Hidden Markov Model-based named entity recognizer effectively to biomedical domain. We integrate various features, including simple deterministic features, morphological features, POS features and semantic trigger features, to capture various evidences especially for biomedical named entity and evaluate their contributions. We also present a simp...

The availability of huge amount of biomedical literature over the Web offers a big opportunity to carry out useful information about published research results. Nevertheless, these information are often enclosed in unstructured documents stressing the need to define suitable framework to support execution of analytics services and richer information discovery tasks. This work introduces a gener...

Automatic extracting protein–protein interaction information from biomedical literature can help to build protein relation network, predict protein function and design new drugs. This paper presents a protein–protein interaction extraction system BioPPIExtractor for biomedical literature. This system applies Conditional Random Fields model to tag protein names in biomedical text, then uses a li...