Currently, there are many classification systems. Broadly speaking, these systems fall into two main categories. These are binary and multiclass systems. Binary classification systems are only concerned with classifying documents into two main categories or groups. Classification systems of this kind are used to distinguish between just two classes of objects. As Maranis and Bebenko (2009) explain, these systems provide Yes/No answer to the question: Does this document belong to class X? In this, such systems can be useful in classifying emails where they are classified whether spam or not, or commercial transactions where they are determined to be fraudulent or not. In such applications, it is more likely and easier to use binary classification systems as we have only two classes or groups. Multiclass systems, in turn, divide documents into two classes or more. As the name indicates, these classifiers assign each document or data point to one of many classes where each has a distinct subject area. Newspaper accounts, for instance, can be classified under different categories such as news, sport, culture, business & money, politics, science, etc. This thesis is only concerned with text clustering. That is, it makes no priori assumptions about the interrelationships of Hardy’s prose works. Computational methods of text clustering fall into two main categories. These are linguistic and statistical mathematical methods (Srivastava and Sahami, 2009; Justo and Torres, 2005). Linguistic methods are based on natural language processing techniques. Methods of this kind usually involve morphological and syntactic processes for extracting meaning and identifying relationships within documents. Mathematical and statistical classificatio... ... middle of paper ... ...sks including SenseClusters (Purandare and Pedersen, 2004). This and others are programs that allow users to cluster similar contexts such as emails and web pages (Pedersen, 2008). The working principle of such programs is that data documents can be grouped on the basis of their mutual contextual similarities (Purandare and Pedersen, 2004). Programs of this kind have indeed proven a successful clustering method when applied to web pages and its merits are more tangible with multimedia material. Nevertheless, an approach of this kind carries with it some limitations. One of them- perhaps the most important- is that it is not concerned with the analysis of the content of documents. One more drawback is that in almost all context classification applications “identical replications of controlled experiments result in different conclusions” (Martin et al., 2005: 470).
1. What is the name of the document? Ida Tarbell Criticizes Standard Oil (1904) 2. What type of document is it? (newspaper, map, image, report, Congressional record, etc.)
The Viterbi algorithm analyzes English text. Probabilities are assigned to each word in the context of the sentence. A Hidden Markov Model for English syntax is used in which the probability of the word is dependent on the previous word or words. The probability of word or words followed by a word in the sentence the probability is calculated for bi-gram, tri-gram and 4-gram.Depending on the length of the sentence the probability is calculated for n-grams [1].
Sorting Things Out: Classification and Its Consequences was written by Geoffrey Bowker and Susan Leigh Star in 1999 and published by Massachusetts Institute of Technology. This work, specifically the introduction discusses the idea of classification and how its patterns are a result of human nature. The authors argue that ultimately the reason we classify can be attributed to human qualities. This thesis is supported by relevant examples in our own lives. For example, the authors write about the classification found in a modern home from the fabric of the furniture to the various codes of building permits allowed. The act of classifying, according to the authors, is almost unconscious. They take this idea a step further by describing the process of classifying as being invisible. The introduction ultimately sets up a foundation for the authors to examine information infrastructures by using classification examples such as the International Classification of Diseases (ICD) and the Nursing Interventions Classification. Their goal is to question why and how classification plays a role in life and human interaction.
For my proposal, I will be conducting my research using the general method of Textual Analysis. Textual Analysis is a path for specialists to assemble data about how other people comprehend the world. It is a strategy - an information gathering process - for those analysts who need to comprehend the routes in which individuals from different societies and subcultures understand their identity, and of how they fit into the world in which they live. Textual Analysis is valuable for specialists working in social investigations, media thinks about, in mass correspondence, what's more, maybe even in human science and reasoning. When we perform a textual analysis on a text, we make an
names and for a short time even spelt his last name as Dui. Dewey died
Document clustering is the process of organizing a particular electronic corpus of documents into subgroups of similar text features. Previously, a number of statistical algorithms had been applied to perform clustering to the data including the text documents. There are recent endeavors to enhance the performance of the clustering with the optimization based algorithms such as the evolutionary algorithms. Thus, document clustering with evolutionary algorithms became an emerging topic that gained more attention in the recent years. This paper presents an up-to-date review fully devoted to evolutionary algorithms designed for document clustering. Its firstly provides comprehensive inspection to the document clustering model revealing its various components and related concepts. Then it shows and analyzes the principle research work in this topic. Finally, it brings together and classifies various objective functions from the collection of research papers. The paper ends up by addressing some important issues and challenges that can be subject of future work.
CYC is a very large, multi-contextual knowledge base and inference engine. The development of CYC was started at the Microelectronics and Computer Technology Corporation(MCC) during the early 1980s and continued at Cycorp, Inc. On January 1, 1995 at Austin, Texas. Doug Lenat, the former head of the CYC Project at MCC and the president of Cycorp at present, has lead the development of the CYC project from the beginning. The goal of the Cyc project is to break the software brittleness bottleneck once and for all by constructing a foundation of basic common sense knowledge system and semantic substratum of terms, rules, and relations that will enable a variety of knowledge-intensive products and services. T...
Cluster analysis can be viewed as dividing similar objects or data into categories or groups (clusters) that are meaningful, useful or both. Cluster analysis is very useful concept for data summarization. When it comes to design meaningful clusters, natural structure of data are considered. Human beings have skills for dividing objects into similar groups and assigning particular objects into those groups. Cluster analysis is applied in practical scenarios
Knowledge from literary sources is transferred into knowledge case with the help of text understanding programs (text recognition or identification programs).
Stylometry is a quantitative investigation into the characteristics of an author’s style. Lann (1995) defines the term as a technique “to grasp the often elusive character of an author's style, or at least part of it, by quantifying some of its features” (1995:271). Matthews and Merriam (1993) agree claiming “Stylometry attempts to capture quantitatively the essence of an individual’s use of language” (1993:203). To put it simply, stylometric analysis is an approach to the investigation of characteristics within literary works through numerical quantitative methods. The relationship between quantitative aspects and literary phenomena is very old. Numerous studies have attempted to explain the stylistic and linguistic properties of authors in terms of quantitative methods and these have been more developed with the availability of computational methods since these methods are accepted by many as more accurate than non-computational ones.
Halliday & Hasan (1976), McCarthy (1991) and others suggest that there are different kind of ties that interrelates texts which are ellipsis/substitution, conjunction, reference, general lexical cohesion, instantial lexical cohesion and thematic patterning.
The field of Computational Linguistics is relatively new; however, it contains several sub-areas reflecting practical applications in the field. Machine (or Automatic) Translation (MT) is one of the main components of Computational Linguistics (CL). It can be considered as an independent subject because people who work in this domain are not necessarily experts in the other domains of CL. However, what connects them is the fact that all of these subjects use computers as a tool to deal with human language. Therefore, some people call it Natural Language Processing (NLP). This paper tries to highlight MT as an essential sub-area of CL. The types and approaches of MT will be considered, and limitations discussed.
Text linguistics is a “discipline which analyses the linguistic regularities and constitutive features of texts” (Bussmann, 1996: 1190). According to this definition, text linguistics is mainly concerned with studying the features that every piece of writing should have in order to be considered as a text. It is also defined by Noth (1977 in Al-Massri, 2013:33) as “the branch of linguistics in which the methods of linguistic analysis are extended to the level of text.” This means that text linguistics aims at producing rules and methods that can be used to analyze the whole text. This approach has been put forward by the two scholars Robert-Alain de Beaugrande and Wolfgang U. Dressler in their seminal book “Introduction to Text Linguistics”, in 1981. The study of texts in linguistic studies starts in
... applied on different Domain data sets and sub level data sets. The data sets are applied on Maximum entropy, Support Vector Machine Method, Multinomial naïve bayes algorithms, I got 60-70% of accuracy. The above is also applied for the Unigrams of Maximum entropy, Support Vector Machine Method, Multinomial naïve bayes algorithms achieved an accuracy of 65-75%. Applied the same data on proposed lexicon Based Semantic Orientation Analysis Algorithm, we received better accuracy of 85%. In subjective Feature Relation Networks Chi-square model using n-grams, POS tagging by applying linguistic rules performed with highest accuracy of 80% to 93% significantly better than traditional naïve bayes with unigram model. The after applying proposed model on different sets the results are validated with test data and proved our methods are more accurate than the other methods.
In the record of the web log server, clustering will be carry out to identify and group the information such as gender, name, phone number, e-mail address and so on into cluster. This will help the website to always keep contact with the users and know about their needs in order to exploit the website business market and also improve the web presence.