Measuring the Relatedness between Documents in Comparable Corpora

Proceedings. Tenth International Workshop on Database and Expert Systems Applications. DEXA 99, 1999

The design of efficient textual similarities is an important issue in the domain of textual data exploration. Textual similarities are for example central in document collection structuring (e.g. clustering), or in Information Retrieval (IR) which relies on the computation of textual similarities for measuring the adequacy between a query and documents.

International Journal of Computer Applications, 2016

Sentence similarity is considered the basis of many natural language tasks such as information retrieval, question answering and text summarization. The semantic meaning between compared text fragments is based on the words' semantic features and their relationships. This article reviews a set of word and sentence similarity measures and compares them on benchmark datasets. On the studied datasets, results showed that hybrid semantic measures perform better than both knowledge and corpus based measures.

arXiv (Cornell University), 2022

The ability to compare the semantic similarity between text corpora is important in a variety of natural language processing applications. However, standard methods for evaluating these metrics have yet to be established. We propose a set of automatic and interpretable measures for assessing the characteristics of corpus-level semantic similarity metrics, allowing sensible comparison of their behavior. We demonstrate the effectiveness of our evaluation measures in capturing fundamental characteristics by evaluating them on a collection of classical and state-of-the-art metrics. Our measures revealed that recentlydeveloped metrics are becoming better in identifying semantic distributional mismatch while classical metrics are more sensitive to perturbations in the surface text levels. * denotes equal contribution.

Journal of Artificial Intelligence Research, 2010

The computation of relatedness between two fragments of text in an automated manner requires taking into account a wide range of factors pertaining to the meaning the two fragments convey, and the pairwise relations between their words. Without doubt, a measure of relatedness between text segments must take into account both the lexical and the semantic relatedness between words. Such a measure that captures well both aspects of text relatedness may help in many tasks, such as text retrieval, classification and clustering. In this paper we present a new approach for measuring the semantic relatedness between words based on their implicit semantic links. The approach exploits only a word thesaurus in order to devise implicit semantic links between words. Based on this approach, we introduce Omiotis, a new measure of semantic relatedness between texts which capitalizes on the word-to-word semantic relatedness measure (SR) and extends it to measure the relatedness between texts. We gradually validate our method: we first evaluate the performance of the semantic relatedness measure between individual words, covering word-to-word similarity and relatedness, synonym identification and word analogy; then, we proceed with evaluating the performance of our method in measuring text-to-text semantic relatedness in two tasks, namely sentence-to-sentence similarity and paraphrase recognition. Experimental evaluation shows that the proposed method outperforms every lexicon-based method of semantic relatedness in the selected tasks and the used data sets, and competes well against corpus-based and hybrid approaches.

Semantic similarity and semantic relatedness measure in particular is very important in the current scenario due to the huge demand for natural language processing based applications such as chatbots and information retrieval systems such as knowledge base based FAQ systems. Current approaches generally use similarity measures which does not use the context sensitive relationships between the words. This leads to erroneous similarity predictions and is not of much use in real life applications. This work proposes a novel approach that gives an accurate relatedness measure of any two words in a sentence by taking their context into consideration. This context correction results in a more accurate similarity prediction which results in higher accuracy of information retrieval systems.

Soft Computing, 2021

Short text similarity measurement methods play an important role in many applications within natural language processing. This paper reviews the research literature on short text similarity (STS) measurement method with the aim to (i) classify and give a broad overview of existing techniques; (ii) find out its strengths and weaknesses in terms of the domain the independence, language independence, requirement of semantic knowledge, corpus and training data, ability to identify semantic meaning, word order similarity and polysemy; and (iii) identify semantic knowledge and corpus resource that can be utilized for the STS measurement methods. Furthermore, our study also considers various issues such as the difference between the various text similarity methods and the difference between semantic knowledge sources and corpora for text similarity. Although there are a few review papers in this area, they focus mostly only on one/two existing techniques. Furthermore, existing review papers do not cover recent research. To the best of our knowledge, this is a comprehensive systematic literature review on this topic. The findings of this research can be as follows: It identified four semantic knowledge and eight corpus resources as external resources that can be classified into general-purpose and domain-specific. Furthermore, the existing techniques can be classified into string-based, corpus-based, knowledge-based and hybrid-based. Moreover, expert researchers can utilize this review as a benchmark as well as reference to the limitations of current techniques. The paper also identifies the open issues that can be considered as feasible opportunities for future research directions. Keywords Natural language processing Á Text mining Á Linguistics Á Similarity measures Á Semantics Á Syntax Communicated by V. Loia.

Measures of Semantic Relatedness (MSRs) provide models of human semantic associations and, as such, have been applied to predict human text comprehension ). In addition, MSRs form key components in more integrated cognitive modeling such as models that perform information search on the World Wide Web (WWW) . However, the effectiveness of an MSR depends on the algorithm it uses as well as the text corpus on which it is trained. In this paper, we examine the impact of corpus selection on the performance of two popular MSRs, Pointwise Mutual Information and Normalised Google Distance. We tested these measures with corpora derived from the WWW, books, news articles, emails, web-forums, and encyclopedia. Results indicate that for the tested MSRs, the traditionally employed books and WWW-based corpora are less than optimal, and that using a corpus based on the New York Times news articles best predicts human behavior.

Estimating word relatedness is essential in natural language processing (NLP), and in many other related areas. Corpus-based word relatedness has its advantages over knowledge-based supervised measures. There are many corpus-based measures in the literature that can not be compared to each other as they use a different corpus. The purpose of this paper is to show how to evaluate different corpus-based measures of word relatedness by calculating them over a common corpus (i.e., the Google n-grams) and then assessing their performance with respect to gold standard relatedness datasets. We evaluate six of these measures as a starting point, all of which are re-implemented using the Google n-gram corpus as their only resource, by comparing their performance in five different data sets. We also show how a word relatedness measure based on a web search engine can be implemented using the Google n-gram corpus.

International Journal of Computer Applications, 2013

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 Knowledgebased similarities. Furthermore, samples of combination between these similarities are presented.

2019

In the last decade, many diverse advances have occurred in the field of information extraction from data. Information extraction in its simplest form takes place in computing environments, where structured data can be extracted through a series of queries. The continuous expansion of quantities of data have therefore provided an opportunity for knowledge extraction (KE) from a textual document (TD). A typical problem of this kind is the extraction of common characteristics and knowledge from a group of TDs, with the possibility to group such similar TDs in a process known as clustering. In this paper we present a technique for such KE among a group of TDs related to the common characteristics and meaning of their content. Our technique is based on the Spearman's Rank Correlation Coefficient (SRCC), for which the conducted experiments have proven to be comprehensive measure to achieve a high-quality KE.