A Review on Identification of Contextual Similar Sentences

The task of identifying contextual similar sentences plays a crucial role in various natural language processing applications such as information retrieval, paraphrase detection, and question answering systems. This paper presents a comprehensive review of the methodologies, techniques, and advancements in the identification of contextual similar sentences. Beginning with an overview of the importance and challenges associated with this task, the paper delves into the various approaches employed, including traditional similarity metrics, deep learning architectures, and transformer-based models. Furthermore, the review explores different datasets and evaluation metrics used to assess the performance of these methods. Additionally, the paper discusses recent trends, emerging research directions, and potential applications in the field. By synthesizing existing literature, this review aims to provide researchers and practitioners with insights into the state-of-the-art techniques and future avenues for advancing the identification of contextual similar sentences

A Comparative Study of Machine Learning Approaches- SVM and LS-SVM using a Web Search Engine Based Application

Abstract — Semantic similarity refers to the concept by which a set of documents or words within the documents are assigned a weight based on their meaning. The accurate measurement of such similarity plays important roles in Natural language Processing and Information Retrieval tasks such as Query Expansion and Word Sense Disambiguation. Page counts and snippets retrieved by the search engines help to measure the semantic similarity between two words. Different similarity scores are calculated for the queried conjunctive word. Lexical pattern extraction algorithm identifies the patterns from the snippets. Two machine learning approaches- Support Vector Machine and Latent Structural Support Vector Machine are used for measuring semantic similarity between two words by combining the similarity scores from page counts and cluster of patterns retrieved from the snippets. A comparative study is made between the similarity results from both the machines. SVM classifies between synonymous and non-synonymous words using maximum marginal hyper plane. LS-SVM shows a much more accurate result by considering the latent values in the dataset

Web-Based Measure of Semantic Relatedness

Semantic relatedness measures quantify the degree in which some words or concepts are related, considering not only similarity but any possible semantic relationship among them. Relatedness computation is of great interest in different areas, such as Natural Language Processing, Information Retrieval, or the Semantic Web. Different methods have been proposed in the past; however, current relatedness measures lack some desirable properties for a new generation of Semantic Web applications: maximum coverage, domain independence, and universality. In this paper, we explore the use of a semantic relatedness measure between words, that uses the Web as knowledge source. This measure exploits the information about frequencies of use provided by existing search engines. Furthermore, taking this measure as basis, we define a new semantic relatedness measure among ontology terms. The proposed measure fulfils the above mentioned desirable properties to be used on the Semantic Web. We have tested extensively this semantic measure to show that it correlates well with human judgment, and helps solving some particular tasks, as word sense disambiguation or ontology matching

Computing semantic similarity measure between words using web search engine

Semantic Similarity measures between words plays an important role in information retrieval, natural language processing and in various tasks on the web. In this paper, we have proposed a Modified Pattern Extraction Algorithm to compute the supervised semantic similarity measure between the words by combining both page count method and web snippets method. Four association measures are used to find semantic similarity between words in page count method using web search engines. We use a Sequential Minimal Optimization (SMO) support vector machines (SVM) to find the optimal combination of page counts-based similarity scores and top-ranking patterns from the web snippets method. The SVM is trained to classify synonymous word-pairs and non-synonymous word-pairs. The proposed Modified Pattern Extraction Algorithm outperforms by 89.8 percent of correlation value

MEASUREMENT OF SEMANTIC SIMILARITY BETWEEN WORDS: A SURVEY

ABSTRACT Semantic similarity measures between words play an important role in community minin

Web search engine based semantic similarity measure between words using pattern retrieval algorithm

Semantic Similarity measures plays an important role in information retrieval, natural language processing and various tasks on web such as relation extraction, community mining, document clustering, and automatic meta-data extraction. In this paper, we have proposed a Pattern Retrieval Algorithm [PRA] to compute the semantic similarity measure between the words by combining both page count method and web snippets method. Four association measures are used to find semantic similarity between words in page count method using web search engines. We use a Sequential Minimal Optimization (SMO) support vector machines (SVM) to find the optimal combination of page counts-based similarity scores and top-ranking patterns from the web snippets method. The SVM is trained to classify synonymous word-pairs and nonsynonymous word-pairs. The proposed approach aims to improve the Correlation values, Precision, Recall, and F-measures, compared to the existing methods. The proposed algorithm outperforms by 89.8% of correlation value

A Survey on Important Aspects of Information Retrieval

Information retrieval has become an important field of study and research under computer science due to the explosive growth of information available in the form of full text, hypertext, administrative text, directory, numeric or bibliographic text. The research work is going on various aspects of information retrieval systems so as to improve its efficiency and reliability. This paper presents a comprehensive survey discussing not only the emergence and evolution of information retrieval but also include different information retrieval models and some important aspects such as document representation, similarity measure and query expansion