Abstract-An important amount of clinical data concerning the medical history of a patient is in the form of clinical reports that are written by doctors. They describe patients, their pathologies, their personal and medical histories, findings made during interviews or during procedures, and so forth. They represent a source of precious information that can be used in several applications such as research information to diagnose new patients, epidemiological studies, decision support, statistical analysis, and data mining. But this information is difficult to access, as it is often in unstructured text form. To make access to patient data easy, our research aims to develop a system for extracting information from unstructured text. In a previous work, a rule-based approach is applied to a clinical reports corpus of infectious diseases to extract structured data in the form of named entities and properties. In this paper, we propose the use of a Boolean inference engine, which is based on a cellular automaton, to do extraction. Our motivation to adopt this Boolean modeling approach is twofold: first optimize storage, and second reduce the response time of the entities extraction.

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Sentiment analysis becomes a very active research area in the text mining field. It aims to extract people's opinions, sentiments, and subjectivity from the texts. Sentiment analysis can be performed at three levels: at document level, at sentence level and at aspect level. An important part of research effort focuses on document level sentiment classification, including works on opinion classification of reviews. This survey paper tackles a comprehensive overview of the last update of sentiment analysis at document level. The main target of this survey is to give nearly full image of sentiment analysis application, challenges and techniques at this level. In addition, some future research issues are also presented.

Precise wind energy potential assessment is vital for wind energy generation and planning and development of new wind power plants. This work proposes and evaluates a novel two-stage method for location-specific wind energy potential assessment. It combines accurate statistical modelling of annual wind direction distribution in a given location with supervised machine learning of efficient estimators that can approximate energy efficiency coefficients from the parameters of optimized statistical wind direction models. The statistical models are optimized using differential evolution and energy efficiency is approximated by evolutionary fuzzy rules.

-Information Extraction (IE) is a natural language processing (NLP) task whose aim is to analyze texts written in natural language to extract structured and useful information such as named entities and semantic relations linking these entities. Information extraction is an important task for many applications such as bio-medical literature mining, customer care, community websites, and personal information management. The increasing information available in patient clinical reports is difficult to access. As it is often in an unstructured text form, doctors need tools to enable them access to this information and the ability to search it. Hence, a system for extracting this information in a structured form can benefits healthcare professionals. The work presented in this paper uses a local grammar approach to extract medical named entities from French patient clinical reports. Experimental results show that the proposed approach achieved an F-Measure of 90. 06%.

Due to the increased volume of Arabic opinionated posts on different social media, Arabic sentiment analysis is viewed as an important research field. Identifying the target or the topic on which opinion has been expressed is the aim of this work. Opinion target identification is a problem that was generally very little treated in Arabic text. In this paper, an opinion target extraction method from Arabic tweets is proposed. First, as a preprocessing phase, several feature forms from tweets are extracted to be examined. The aim of these forms is to evaluate their impacts on accuracy. Then, two classifiers, SVM and Naïve Bayes are trained. The experiment results show that, with 500 tweets collected and manually tagged, SVM gives the highest precision and recall (86%).

Information Extraction (IE) is a natural language processing (NLP) task whose aim is to analyse texts written in natural language to extract structured and useful information such as named entities and semantic relations between them. Information extraction is an important task in a diverse set of applications like bio-medical literature mining, customer care, community websites, personal information management and so on. In this paper, the authors focus only on information extraction from clinical reports. The two most fundamental tasks in information extraction are discussed; namely, named entity recognition task and relation extraction task. The authors give details about the most used rule/pattern-based and machine learning techniques for each task. They also make comparisons between these techniques and summarize the advantages and disadvantages of each one.

Sentiment analysis is a text mining discipline that aims to identify and extract subjective information. This growing field results in the emergence of three levels of granularity (document, sentence, and aspect). However, both the document and sentence levels do not find what exactly the opinion holder likes and dislikes. Furthermore, most research in this field deals with English texts, and very limited researches are undertaken on Arabic language. In this paper, the authors propose a semantic aspect-based sentiment analysis approach for Arabic reviews. This approach utilizes the semantic of description logics and linguistic rules in the identification of opinion targets and their polarity.