An Approach towards Data Clustering By Using NLP and Annotated Text Categorization

Aim is to develop system for clustering of data into user defines clusters with the help of language processing. The main objective behind this research is to solve the problem of data classification into large dataset to get an efficient system which classifies data not only on basis of the dataset, but also on basis of the property of keyword and specified class. This provides the best optimization and segmentation, which incorporate a priori knowledge of existing dataset. This will help end user to choose the item from the particular data cluster from its previous parches or search from the dataset. This field leads to: event resolution, grammar annotation, information mining, knowledgebase, labeling, question/answer, redundancy reduction, similarity measure, summarization, word sense disambiguation, and word sense induction. Implementation of application of Apriory algorithm on the given data to classify the data into the categories. Bisecting K-Means algorithm and hierarchical clustering used categorizing all objects in single cluster. PDDP is the latest development of SVD-based partitioning techniques. DOI: 10.17762/ijritcc2321-8169.15079

Reading Between the Lines: Verifying Mathematical Language

A great deal of work has been done on automatically generating automated proofs of formal statements. However, these systems tend to focus on logic-oriented statements and tactics as well as generating proofs in formal language. This project examines proofs written in natural language under a more general scope of mathematics. Furthermore, rather than attempting to generate natural language proofs for the purpose of solving problems, we automatically verify human-written proofs in natural language. To accomplish this, elements of discourse parsing, semantic interpretation, and application of an automated theorem prover are implemented

A Word Sense Disambiguation Approach for Converting Natural Language Text into a Common Semantic Description

Abstract—Concept Description Language (CDL) is a common language that represents the semantics of content in a simple and structured manner. In particular, it is intended to describe Natural Language (NL) texts in a format that can be understood and processed by computers. Since words with multiple meanings can be found from texts, it becomes necessary to perform Word Sense Disambiguation (WSD) in order to achieve a correct representation. This paper presents a WSD approach that determines best candidates for word meanings and contributes to a semi-automatic conversion of NL into CDL. We perform preliminary experiments by evaluating the approach with some test sentences and comparing with other WSD methods. Results suggest that the existence of a proper correspondence of syntactic and semantic relations for the WSD process may lead to an accurate conversion to CDL. Keywords-Semantic computing; Concept description; Natural language text