The investigation presented in this paper is a novel method in QUESTION answering (QA) that enables a QA system to gain performance through reuse of information in the answer to one QUESTION to answer another related QUESTION. Our analysis shows that a pair of QUESTION in a general open domain QA can have embedding relation through their mentions of noun phrase expressions. We present methods for recognition of embedding property between a pair of QUESTIONs by focusing on the techniques applied to match noun phrase mentions in the QUESTIONs. We then take advantage from the discovery of embedding relationship and extract referent named entities corresponding to the noun phrase expression that are present in the answer of one QUESTION. The named entities will then be used as significant terms in the query generation phase of the QA system to retrieve more pertinent answers. Finally, we discuss on data set resources and system evaluation.

There are several meanings for the term hermeneutics on the basis of its application.The uses of the word should be precisely clarified their meaning and the amis by using this term. Criticism domain of artwork has had the most damage due to the peculiar application of the word hermeneutics and its different equivalents such as interpretation, analysis of critics and rhetoricians and thinkers in their written works. If the meaning of the words in their applications and the users intention of the words is not clarified there would happen no immediate and clear action would be at hand, moreover the written works readers would be mislead in understanding the have wrong interpretation to that of the authors. Interpretation and understanding a phenomenon is impossible as for as some thinkers take understanding and interpretation as a unique entity. Human mental capabilities are in form that allows making meaning for every phenomenon or seek to discover the meaning. Such capacity is called as interpretive capacity. Texts are unveiled through interpretation.The aim of text interpretation and understanding is to get its meaning, meaning is the fruit of understanding.Dasani is an interpret or that has heaven and innately interprets the phenomenon. In order to interpret a text it is necessary to have a method. The text direct the interpreters in finding the methods of interpretation, in other words texts are direction of interpreters for interpretation. Studying the definitions of hermeneutics and analyzing the interpretation process nature is necessary for any researcher. Because interpretation, cognition and cosmology have innate relation and in addition to this, interpretation in art domain appears in the form of art critics. The QUESTION of what is hermeneutics and studying the general problems related to interpretation is the gist of this article.

Any litigation is composed of a series of facts and rules the distinction between them is of great import. One may distinct the factual elements of litigation as "QUESTIONs of fact" from the legal elements thereof classically called "QUESTIONs of law". The parties to litigation as well as the judges who adjudicate the litigation always need to keep this distinction in mind to avoid misleading the process of adjudication. "The due process of law" requires that both the litigants and the judge play their own role in the proceeding; while the litigants are to deal with the QUESTIONs of fact, the judge shall exclusively determine the QUESTIONs of law. This essay tries to draw a clear and pragmatic border between the QUESTIONs of fact and QUESTIONs of law and present a logically well-defined criterion appropriate to apply by the tribunals and judicial officers in this effect.

A QUESTION Answering System (QAS) is a special form of information retrieval that consists of three parts: QUESTION processing, information retrieval, and answer selection. Determining the type of QUESTION is the most important part of QAS as it affects the other following parts. In this work, we use the effective features and ensemble classification in order to improve the QAS performance by increasing the accuracy of QUESTION type identification. We use the Gravitational Search Algorithm (GSA) in order to select the features and perform the ensemble classification. The proposed system is extensively tested on different datasets using four types of experiments: (1) neither feature selection nor ensemble classification, (2) feature selection without ensemble classification, (3) ensemble classification without feature selection, and (4) feature selection with ensemble classification. These four kinds of experiments are carried out under the Differential Evolution (DE) algorithm and GSA. The experimental results obtained show that the proposed method outperforms compared to the state-of-the-art methods in the previous research works.

QUESTION answering systems are produced and developed to provide exact answers to the QUESTION posted in natural language. One of the most important parts of QUESTION answering systems is QUESTION classification. The purpose of QUESTION classification is predicting the kind of answer needed for the QUESTION in natural language. The literature works can be categorized as rule-based and learning-based methods. This paper proposes a novel architecture for hybrid classification of QUESTIONs. The results of the classifiers were combined by five methods of Weighted Voting, Behavior Knowledge space, Naive Bayes, Decision Template and Dempster-Shafer. The method uses a combination of two classifiers based on machine learning (Support Vector Machine and Sparse Representation) and one rule-based classifier. The learning-based classification uses the set of features extracted from the QUESTIONs. The features are extracted on the basis of the lexical and syntactic structure of the QUESTIONs. The results from the classifiers were combined by the methods that are common in the combination of one-class classifiers and the Obtained results indicate the improvement of the classification operations in comparison with the present methods.