Measuring the similarity between words, sentences, paragraphs and documents is an important component in various tasks such as information retrieval, document clustering, word-sense disambiguation, automatic essay scoring, short answer grading, machine translation and text summarization. This survey discusses the existing works on text similarity through partitioning them into three approaches; String-based, Corpus-based and Knowledge-based similarities. Furthermore, samples of combination between these similarities are presented. General Terms Text Mining, Natural Language Processing. Keywords BasedText Similarity, Semantic Similarity, String-Based Similarity, Corpus-Based Similarity, Knowledge-Based Similarity. NeedlemanWunsch 1. INTRODUCTION Text similarity measures play an increasingly important role in text related research and applications in tasks Nsuch as information retrieval, text classification, document clustering, topic detection, topic tracking, questions generation, question answering, essay scoring, short answer scoring, machine translation, text summarization and others. Finding similarity between words is a fundamental part of text similarity which is then used as a primary stage for sentence, paragraph and document similarities. Words can be similar in two ways lexically and semantically. Words are similar lexically if they have a similar character sequence. Words are similar semantically if they have the same thing, are opposite of each other, used in the same way, used in the same context and one is a type of another. DistanceLexical similarity is introduced in this survey though different String-Based algorithms, Semantic similarity is introduced through Corpus-Based and Knowledge-Based algorithms. String-Based measures operate on string sequences and character composition. A string metric is a metric that measures similarity or dissimilarity (distance) between two text strings for approximate string matching or comparison. Corpus-Based similarity is a semantic similarity measure that determines the similarity between words according to information gained from large corpora. Knowledge-Based similarity is a semantic similarity measure that determines the degree of similarity between words using information derived from semantic networks. The most popular for each type will be presented briefly. This paper is organized as follows: Section two presents String-Based algorithms by partitioning them into two types character-based and term-based measures. Sections three and four introduce Corpus-Based and knowledge-Based algorithms respectively. Samples of combinations between similarity algorithms are introduced in section five and finally section six presents conclusion of the survey.

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A Survey of Text Similarity Approaches

Environmental problems have contributed to numerous collapses of civilizations in the past. Now, for the first time, a global collapse appears likely. Overpopulation, overconsumption by the rich and poor choices of technologies are major drivers; dramatic cultural change provides the main hope of averting calamity.

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Can a collapse of global civilization be avoided

availability of enough visualization techniques it can be very confusing to know what and when should be appropriate technique to use in order to convey maximum possible understanding. The basic purpose of visual representation is to efficiently interpret what is insight, as easy as possible. Different available visualization techniques are use for different situation which convey different level of understanding. This document is guide for the young researchers who wants to start work in visualization. The purpose of this piece of document is to collect all visualization techniques with their brief introduction. This paper deals with many definitions and aspects of visualization, how visualization take place i.e. different steps of visualization process, problems that are confront in visualization, categorization of visualization techniques on the bases of distinct perspective, typically known common data and information visualization techniques, basic interactive methods for visualization their advantages and disadvantages, interactivity process, and the scope of visualization up to some extent in different field of research. General Terms Visualization, Visualization techniques, Challenges, Interactive techniques.

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Data and Information Visualization Methods, and Interactive Mechanisms: A Survey

the queried object, from a large volume of given uncertain dataset, is a tedious task which involves time complexity and computational complexity. To solve these complexities, various research techniques were proposed. Among these, the simple, highly efficient and effective technique is, finding the K-Nearest Neighbor (kNN) algorithm. It is a technique which has applications in various fields such as pattern recognition, text categorization, moving object recognition etc. Different kNN techniques are proposed by various researchers under various situations. In this paper, we classified these techniques into two ways: (1) structure based (2) non-structure based kNN techniques. The aim of this paper is to analyze the key idea, merits, demerits and target data behind each kNN techniques. The structure based kNN techniques such as Ball Tree, k-d Tree, Principal Axis Tree (PAT), Orthogonal Structure Tree (OST), Nearest Feature Line (NFL), Center Line (CL) and Non-structured kNN techniques such as Weighted kNN, Condensed NN, Model based k-NN, Ranked NN (RNN), Pseudo/Generalized NN, Clustered k- NN(CkNN), Mutual kNN (MkNN), Constrained RkNN etc., are analyzed in this paper. It is observed that the structure based kNN techniques suffer due to memory limit whereas the Non-structure based kNN techniques suffer due to computation complexity. Hence, structure based kNN techniques can be applied to small volume of data whereas Non-structure kNN techniques can be applied to large volume of data.

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A Review of various k-Nearest Neighbor Query Processing Techniques

th job to the i th person. The cost is usually deterministic in nature. In this paper aij has been considered to be trapezoidal and triangular numbers denoted by aij which are more realistic and general in nature. Robust’s ranking method [10] has been used for ranking the fuzzy numbers. The fuzzy assignment problem has been transformed into crisp assignment problem in the linear programming problem form and solved by using Hungarian method; Numerical examples show that the fuzzy ranking method offers an effective tool for handling the fuzzy assignment problem.

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Marjorie V. Batey

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THE TWO NORMATIVE WORLDS of the University Nursing Faculty

The Student And The Unwed Mother: A Productive Learning Experience