A Survey of Text Similarity Approaches
18 Apr 2013-International Journal of Computer Applications (Foundation of Computer Science (FCS))-Vol. 68, Iss: 13, pp 13-18
TL;DR: This survey discusses the existing works on text similarity through partitioning them into three approaches; String-based, Corpus-based and Knowledge-based similarities, and samples of combination between these similarities are presented.
Abstract: 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 Knowledge-based similarities. Furthermore, samples of combination between these similarities are presented. General Terms Text Mining, Natural Language Processing. Keywords BasedText Similarity, Semantic Similarity, String-Based Similarity, Corpus-Based Similarity, Knowledge-Based Similarity. NeedlemanWunsch 1. INTRODUCTION Text similarity measures play an increasingly important role in text related research and applications in tasks Nsuch as information retrieval, text classification, document clustering, topic detection, topic tracking, questions generation, question answering, essay scoring, short answer scoring, machine translation, text summarization and others. Finding similarity between words is a fundamental part of text similarity which is then used as a primary stage for sentence, paragraph and document similarities. Words can be similar in two ways lexically and semantically. Words are similar lexically if they have a similar character sequence. Words are similar semantically if they have the same thing, are opposite of each other, used in the same way, used in the same context and one is a type of another. DistanceLexical similarity is introduced in this survey though different String-Based algorithms, Semantic similarity is introduced through Corpus-Based and Knowledge-Based algorithms. String-Based measures operate on string sequences and character composition. A string metric is a metric that measures similarity or dissimilarity (distance) between two text strings for approximate string matching or comparison. Corpus-Based similarity is a semantic similarity measure that determines the similarity between words according to information gained from large corpora. Knowledge-Based similarity is a semantic similarity measure that determines the degree of similarity between words using information derived from semantic networks. The most popular for each type will be presented briefly. This paper is organized as follows: Section two presents String-Based algorithms by partitioning them into two types character-based and term-based measures. Sections three and four introduce Corpus-Based and knowledge-Based algorithms respectively. Samples of combinations between similarity algorithms are introduced in section five and finally section six presents conclusion of the survey.
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01 Jan 2018
TL;DR: Learning to Rank Relevant Files for Bug Reports Using Domain knowledge, Replication and Extension of a Learning-to-Rank approach.
Abstract: Learning to Rank Relevant Files for Bug Reports Using Domain knowledge, Replication and Extension of a Learning-to-Rank Approach
1 citations
Cites methods from "A Survey of Text Similarity Approac..."
...In [7], authors present three approaches: String-based, Corpus-based, and Knowledge-based....
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01 Jul 2020
TL;DR: This work proposed a methodology which is able to recommend similar cases based on content based similarity and the legal citation network and it was observed that the accuracy of the said methodology increases with an increase in the number of recommendations made.
Abstract: Finding related cases is an important aspect of the preparation required for any legal case. These documents are often complicated and important characteristics of the case are hidden. This makes it difficult for the lawyers and paralegals to extract the said characteristics. We propose a methodology which is able to recommend similar cases based on content based similarity and the legal citation network. It was observed that the accuracy of the said methodology increases with an increase in the number of recommendations made.
1 citations
Additional excerpts
...Another metric is the Jaccards similarity index [3, 11, 12] which measures similarity by comparing two sets to...
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06 Nov 2015
TL;DR: This paper presents a novel buggy source file localization approach, leveraging both a part-of-speech based weighting strategy and the invocation relationship among source files, and integrates an adaptive technique to strengthen the optimization of the performance.
Abstract: Given a corpus of bug reports, software developers must read various descriptive sentences in order to identify corresponding buggy source files which potentially result in the defects. This process itself represents one of the most expensive, as well as time-consuming, activities during software maintenance and evolution. To alleviate the workload of developers, many methods have been proposed to automate this process and narrow down the scope of reviewing buggy files. In this paper, we present a novel buggy source file localization approach, leveraging both a part-of-speech based weighting strategy and the invocation relationship among source files. We also integrate an adaptive technique to strengthen the optimization of the performance. The adaptive technique consists of two modules. One is to maximize the accuracy of the first recommended file, and the other aims at improving the accuracy of the fixed defect file list. We evaluate our approach on three large-scale open source projects, i.e., ASpectJ, Eclipse, and SWT. Compared with the baseline work, our approach can improve 17.13%, 6.29% and 3.15% on top 1, top 5 and top 10 respectively for ASpectJ, 6.40%, 4.94% and 4.39% on top 1, top 5 and top 10 respectively for Eclipse, and 15.31%, 8.16% and 5.10% on top 1, top 5 and top 10 respectively for SWT.
1 citations
Cites background from "A Survey of Text Similarity Approac..."
...It is generally known that the smaller the angle of two vectors is, the closer the two documents represented by the two vectors are [8]....
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TL;DR: This work aims to study and compare the performance of four metaheuristic algorithms called Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Imperialist Competitive Algorithm and Fuzzy Imperialist competitive Algorithm to tackle the similarity problem in information retrieval and data mining.
Abstract: The similarity problem, finding a group of objects which have the most similarity to each other, has become an important issue in information retrieval and data mining. The theory of this concept is mathematically proven, but it practically has high time complexity. Binary Genetic Algorithm (BGA) has been applied to improve solutions quality of this problem, but a more efficient algorithm is required. Therefore, we aim to study and compare the performance of four metaheuristic algorithms called Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Imperialist Competitive Algorithm (ICA) and Fuzzy Imperialist Competitive Algorithm (FICA) to tackle this problem. The experiments are conducted on two applications; the former is on four UCI datasets as a general application and the latter is on the text resemblance application to detect multiple similar text documents from Reuters datasets as a case study. The results of experiments give a ranking of the algorithms in solving the -similarity problem in both applications based on the exploration and exploitation abilities, that the FICA achieves the first rank in both applications as well as based on the both criteria.
1 citations
References
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TL;DR: The lexical database: nouns in WordNet, Katherine J. Miller a semantic network of English verbs, and applications of WordNet: building semantic concordances are presented.
Abstract: Part 1 The lexical database: nouns in WordNet, George A. Miller modifiers in WordNet, Katherine J. Miller a semantic network of English verbs, Christiane Fellbaum design and implementation of the WordNet lexical database and searching software, Randee I. Tengi. Part 2: automated discovery of WordNet relations, Marti A. Hearst representing verb alterations in WordNet, Karen T. Kohl et al the formalization of WordNet by methods of relational concept analysis, Uta E. Priss. Part 3 Applications of WordNet: building semantic concordances, Shari Landes et al performance and confidence in a semantic annotation task, Christiane Fellbaum et al WordNet and class-based probabilities, Philip Resnik combining local context and WordNet similarity for word sense identification, Claudia Leacock and Martin Chodorow using WordNet for text retrieval, Ellen M. Voorhees lexical chains as representations of context for the detection and correction of malapropisms, Graeme Hirst and David St-Onge temporal indexing through lexical chaining, Reem Al-Halimi and Rick Kazman COLOR-X - using knowledge from WordNet for conceptual modelling, J.F.M. Burg and R.P. van de Riet knowledge processing on an extended WordNet, Sanda M. Harabagiu and Dan I Moldovan appendix - obtaining and using WordNet.
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TL;DR: A computer adaptable method for finding similarities in the amino acid sequences of two proteins has been developed and it is possible to determine whether significant homology exists between the proteins to trace their possible evolutionary development.
11,844 citations
10,500 citations
"A Survey of Text Similarity Approac..." refers background in this paper
...Dice’s coefficient is defined as twice the number of common terms in the compared strings divided by the total number of terms in both strings [11]....
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TL;DR: This letter extends the heuristic homology algorithm of Needleman & Wunsch (1970) to find a pair of segments, one from each of two long sequences, such that there is no other Pair of segments with greater similarity (homology).
10,262 citations
"A Survey of Text Similarity Approac..." refers background in this paper
...It is useful for dissimilar sequences that are suspected to contain regions of similarity or similar sequence motifs within their larger sequence context [8]....
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TL;DR: A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LSA), is presented and used to successfully simulate such learning and several other psycholinguistic phenomena.
Abstract: How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LSA), is presented and used to successfully simulate such learning and several other psycholinguistic phenomena. By inducing global knowledge indirectly from local co-occurrence data in a large body of representative text, LSA acquired knowledge about the full vocabulary of English at a comparable rate to schoolchildren. LSA uses no prior linguistic or perceptual similarity knowledge; it is based solely on a general mathematical learning method that achieves powerful inductive effects by extracting the right number of dimensions (e.g., 300) to represent objects and contexts. Relations to other theories, phenomena, and problems are sketched.
6,014 citations
"A Survey of Text Similarity Approac..." refers methods in this paper
...The GLSA approach can combine any kind of similarity measure on the space of terms with any suitable method of dimensionality reduction....
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...LSA assumes that words that are close in meaning will occur in similar pieces of text....
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...Latent Semantic Analysis (LSA) [15] is the most popular technique of Corpus-Based similarity....
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...Generalized Latent Semantic Analysis (GLSA) [16] is a framework for computing semantically motivated term and document vectors....
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...Mining the web for synonyms: PMIIR versus LSA on TOEFL....
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