"This book provides relevant theoretical frameworks and the latest empirical research findings in biomedicine information retrieval as it pertains to linguistic granularity"--Provided by publisher.

There is an increasing need throughout the biomedical sciences for a greater understanding of knowledge-based systems and their application to genomic and proteomic research. This book discusses knowledge-based and statistical approaches, along with applications in bioinformatics and systems biology. The text emphasizes the integration of different methods for analysing and interpreting biomedical data. This, in turn, can lead to breakthrough biomolecular discoveries, with applications in personalized medicine. Key Features: Explores the fundamentals and applications of knowledge-based and statistical approaches in bioinformatics and systems biology. Helps readers to interpret genomic, proteomic, and metabolomic data in understanding complex biological molecules and their interactions. Provides useful guidance on dealing with large datasets in knowledge bases, a common issue in bioinformatics. Written by leading international experts in this field. Students, researchers, and industry professionals with a background in biomedical sciences, mathematics, statistics, or computer science will benefit from this book. It will also be useful for readers worldwide who want to master the application of bioinformatics to real-world situations and understand biological problems that motivate algorithms.

Websites are a central part of today’s business world; however, with the vast amount of information that constantly changes and the frequency of required updates, this can come at a high cost to modern businesses. Web Data Mining and the Development of Knowledge-Based Decision Support Systems is a key reference source on decision support systems in view of end user accessibility and identifies methods for extraction and analysis of useful information from web documents. Featuring extensive coverage across a range of relevant perspectives and topics, such as semantic web, machine learning, and expert systems, this book is ideally designed for web developers, internet users, online application developers, researchers, and faculty.

eHealth initiatives, many of which are being undertaken in countries around the globe, have myriad benefits, including improvement of coordination and integration of health care delivery, empowerment of individuals and families for helping them manage their own health better and prepare health care plans, and facilitation of public health initiatives. eHealth are not simply a technology but a complex technological and relational process. In this sense, clinicians and health care providers who seek to successfully exploit eHealth should pay special attention to technology, ergonomics, human factors, and organizational changes associated with the structure of the relevant health service. This book gives an overview of the impact of eHealth systems on access to health care, quality of information on health care, cost-effectiveness of health care services and the development of eHealth equipment.

Computer-assisted translation (CAT) has always used translation memories, which require the translator to have a corpus of previous translations that the CAT software can use to generate bilingual lexicons. This can be problematic when the translator does not have such a corpus, for instance, when the text belongs to an emerging field. To solve this issue, CAT research has looked into the leveraging of comparable corpora, i.e. a set of texts, in two or more languages, which deal with the same topic but are not translations of one another. This work had two primary objectives. The first is to assess the input of lexicons extracted from comparable corpora in the context of a specialized human translation task. The second objective is to identify bilingual-lexicon-extraction methods which best match the translators' needs, determining the current limits of these techniques and suggesting improvements. The author focuses, in particular, on the identification of fertile translations, the management of multiple morphological structures, and the ranking of candidate translations. The experiments are carried out on two language pairs (English–French and English–German) and on specialized texts dealing with breast cancer. This research puts significant emphasis on applicability – methodological choices are guided by the needs of the final users. This book is organized in two parts: the first part presents the applicative and scientific context of the research, and the second part is given over to efforts to improve compositional translation. The research work presented in this book received the PhD Thesis award 2014 from the French association for natural language processing (ATALA).

This book presents the basics and recent advancements in natural language processing and information retrieval in a single volume. It will serve as an ideal reference text for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology. This text emphasizes the existing problem domains and possible new directions in natural language processing and information retrieval. It discusses the importance of information retrieval with the integration of machine learning, deep learning, and word embedding. This approach supports the quick evaluation of real-time data. It covers important topics including rumor detection techniques, sentiment analysis using graph-based techniques, social media data analysis, and language-independent text mining.Features: Covers aspects of information retrieval in different areas including healthcare, data analysis, and machine translation Discusses recent advancements in language- and domain-independent information extraction from textual and/or multimodal data Explains models including decision making, random walk, knowledge graphs, word embedding, n-grams, and frequent pattern mining Provides integrated approaches of machine learning, deep learning, and word embedding for natural language processing Covers latest datasets for natural language processing and information retrieval for social media like Twitter The text is primarily written for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology.