What Is A Hybrid Approach For Label Classification Using Multi-Supervised Learning System

Data is ever increasing. We need a system to represent, store and manipulate complex information, detect correlations and patterns, construct data models etc. Furthermore, being independently maintained, data can change in time or even change its base structure, making it difficult for modelling systems to accommodate these changes. Current representation and storage systems are not very flexible in dealing with structural changes and also they are not powered with the ability of performing complex data manipulations of the sort mentioned above.

There are certain challenges that are to be faced with this zillion of bytes of data; one of the major is integrating and retrieval of data and incorporating with the enterprise system(Dubois, 2001).

With the advent of computer technology in 1990’s the need to search large databases was increasingly becoming vital. The search engines prior to PageRank had limitations, the then most widely used algorithm used text based indexes to provide search results on World Wide Web however had limitations of improper search results as the logic used by the search engines looked at the number of occurrences of the search word in webpage which sometimes resulted in improper search results. Another technique used during the time was based on variations of standard vector space model – i.e. search based on how recent the webpage was updated and/or how close the search terms are to the

Kimura, H., Huo, G., Rasin, A., Madden, S., & Zdonik, S. B. (2010). CORADD: Correlation aware database designer for materialized views and indexes. Proceedings of the VLDB Endowment, 3(1-2), 1103-1113.

Dodds, L. (2001) XML and Databases? Follow Your Nose. Published on XML.com Retrieved from: http://ww

Our tables can be widely classified into fact tables (measurements, e.g., impressions, clicks, conversions) and dimension tables (configuration, e.g., campaigns, lineitems, tactics). While processing data from fact tables, the requirement for related data from

A hierarchical data structure is implemented using multilinked lists that contain scalar items, vectors, and possibly, n-dimensional spaces.Stack is a conceptual model of a data structure that can be implemented as a vector or a linked

Document opinion analysis is about classifying the overall document that have sentiment words expressed by the authors. The task is to determine whether a document is positive, negative or neutral (Wawre & Deshmukh). Document level categorization attempts to classify sentiments in web forum postings, blogs, movie and news

The main purpose is to detect topics automatically and track related documents from a stream of documents temporally so that readers can understand. First stage, Theme Generation process tries to identify the theme of the topic. Next Event Segmentation and Summarization models the documents as a symmetric block association matrix. Eigen vectors are then drawn to examine and extract summaries. Finally, Temporal Similarity (TS) function is used to calculate the event dependencies. This had given me an opportunity to expose my knowledge in Software Engineering and Data Mining. This also helped us to gain domain knowledge and also enhance technical skills like Servlets and JSP, used for implementing main logic, while JDBC for back end database connection and performing basic operations of database and Html for UI

The system crawls all the information (customer reviews) from the particular product and starts to extract the feature words. The mining classification is divided into three subtasks. First, POS tagging, most product feature in reviews contain nouns and phrases. POS enables to identify these tags words by word. The authors have used NLProcessor linguistic parser to split the sentences and produce POS tag for every individual word. Some pre-processing is performed to remove stop words to avoid big POS tag lists. Fuzzy matching was also used to deal with misspelling and word variants. Second, identifying frequent features, features that are particularly used by customers in many reviews. To put in context, words, or sentences that occur together in many reviews. The authors use association mining to list out these words. An apriori algorithm based association miner is used to identify these features/words. The gathered features cannot possibly be genuine product features. Pruning enables to remove the unwanted features/words that don’t match to product opinion or feature. The author uses two pruning methods and compares their efficiency. While using compactness pruning, it looks for features that is composed of two or more words and identifies if they make

Text document processing plays a key role in data mining as well as web search for information retrieval. In text processing, the commonly used model is bag-of-words model [5]. In this model each document is typically represented in vector form in which each element indicates the value of the analogous feature in the document. The feature value can be selected by finding number of occurrences of a term in the document. However relative term frequency can be defined as the ratio between the term frequency and the total number of occurrences of all the terms in the document set. Frequently, the dimensionality of a document is large and the resulting vector is sparse, i.e., most of the selected feature values in the vector are zero. Such high-dimensionality and sparsity is a challenge for similarity measure and thus it is a very important operation in text processing algorithms.

Mihalceaet. al.‟s work was able to break 70% classification accuracy by using a bag-of-words model, and 10-fold cross-validation with Naive Bayes and SVM (support vector machine).Cardieet. al. Using a combination of LIWC, bigram and unigram features, they were capable to achieve 89.8% classification accuracy using 5- fold cross validation with linear SVM on written textual data [5] .

Ontology contains a set of concepts and relationship between concepts, and can be applied into information retrieval to deal with user queries.

The main aim of this project is to research on the integration of “Natural Language Processing “ and information systems engineering to enhance query retrieval in natural language processing.

Support Vector Machine (SVM) is primarily a classier method that performs classification tasks by constructing hyperplanes in a multidimensional space that separates cases of different class labels. SVM supports both regression and classification tasks and can handle multiple continuous and categorical variables. For categorical variables a dummy variable is created with case values as either 0 or 1. Thus, a categorical dependent variable consisting of three levels, say (A, B, C), is represented by a set of three dummy variables: