Motivation: A major goal in biomedical text mining is the extraction of biological entities, associations between them, and their respective mapping to database entries. One common and successful approach is to use sets of linguistic patterns that match, for instance, protein-protein interactions or gene-disease associations in a sentence. Pattern engineering is usually done by hand or relies o...

Biomedical text-mining systems have great promise for improving the efficiency and productivity of biomedical researchers. However, such systems are still not in routine use. One impediment to their development is the lack of systematic and rigorous evaluation, comparable to the approaches developed for information retrieval systems. The developers of text-mining systems need to improve both te...

In this paper, we present a large biomedical term resource automatically compiled from the terminology of a selection of biomedical databases. The resource has a very simple and intuitive format and therefore can be easily embedded into a system for biomedical text mining and used as a linguistic resource. It is continuously updated and a user interface makes it possible to compile a new term r...

The biomedical community makes extensive use of text mining technology. In the past several years, enormous progress has been made in developing tools and methods, and the community has been witness to some exciting developments. Although the state of the community is regularly reviewed, the sheer volume of work related to biomedical text mining and the rapid pace in which progress continues to...

We give an overview of our approach to the extraction of interactions between pharmacogenomic entities like drugs, genes and diseases and suggest classes of interaction types driven by data from PharmGKB and partly following the top level ontology WordNet and biomedical types from BioNLP. Our text mining approach to the extraction of interactions is based on syntactic analysis. We use syntactic...

Data mining is a technique of data evaluation for discovering hidden patterns over the raw data. The data is found in unstructured manner in the real world, therefore for extracting the meaningful data contents form this raw data, the data mining methods and techniques are helpful.In this presented study the, the data mining techniques are utilized for finding the valuable contents from the tex...

Text mining is nothing but extracting useful information from text. The information to be extracted is explicitly stated in the text. Text mining can be applied in various domains like medical, economical, etc. Medical text extraction can be done through medical patient reports, online medical journals, online health communities, etc. Text mining includes different tasks like classification, su...

The proliferation of digitally available textual data necessitates automatic tools for analyzing large textual collections. Thus, in analogy to data mining for structured databases, text mining is defined for textual collections. A central tool in text-mining is the analysis of concept relationship, which discovers connections between different concepts, as reflected in the collection. However,...

Numerous initiatives have allowed users to share knowledge or opinions using collaborative platforms. In most cases, the users provide a textual description of their knowledge, following very limited or no constraints. Here, we tackle the classification of documents written in such an environment. As a use case, our study is made in the context of text mining evaluation campaign material, relat...

With the rapid growth of electronically available scientific literature, text mining is attracting increasing attention. While numerous algorithms, tools, and systems have been developed for extracting information from text, little effort has been focused on how to mark up the information. We present the design of a standoff, object-oriented markup language (called SOOML), which is simple, expr...