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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29 Sep 2022
TL;DR: In this paper , the authors present an approach in order to mitigate the problem of duplicated person instances in vast amounts of data gathered from heterogeneous sources, such as video, audio, text/documents, social media and web data, telecom data, surveillance systems data, and police databases.
Abstract: Investigators all over the world are very often confronted with a variety of complex cases in which huge amounts of data have to be processed and analyzed. The analysis of such data volumes frequently exceeds the capacity of the law enforcement agencies (LEAs) infrastructure. Due to the extraction of information of such volume and velocity from a plethora of sources, there is often the problem of data duplication and similarity among different pieces of information. In this light, the authors of this chapter present an approach in order to mitigate the problem of duplicated person instances in vast amounts of data gathered from heterogeneous sources. The person fusion methodology presented in this chapter, as indicated by its name, aims to fuse different person instances that refer to the same person. These person instances are included in various heterogeneous sources, such as video, audio, text/documents, social media and web data, telecom data, surveillance systems data, and police databases. Person fusion algorithms and techniques combine the collected information on person instances and through the right processing provide feedback to the end users regarding the similarity degree between pairs of persons. In this context, a comparative study between different-sized datasets and algorithms is also conducted.
25 Nov 2020
TL;DR: Py_ape as mentioned in this paper is a package in Python that integrates a number of string and text processing algorithms for collecting, extracting, and cleaning text data from websites, creating frames for text corpora, and matching entities, matching two schemas, mapping and merging two schemata.
Abstract: Py_ape is a package in Python that integrates a number of string and text processing algorithms for collecting, extracting, and cleaning text data from websites, creating frames for text corpora, and matching entities, matching two schemas, mapping and merging two schemas. The functions of Py_ape help the user step-by-step perform data integration and data preparation, based on some popular Python libraries. Especially in the entity matching function of the schema matching and merging phase, we used the Hamming distance algorithm to identify similar string pairs, and the longest common substring similarity algorithm to map data between the columns of schemas. These algorithms help to increase the accuracy of the schema matching process. In addition, in the article, we present experimental results using Py_ape to scrape, clean, match, and merge two sets of data related to aviation crashes, taken from different sources of Kaggle and Wikipedia. The result of the experiment will be evaluated in detail in the rest of the paper.
01 Jan 2020
TL;DR: Experimental results show that the proposed searching framework, i.e., combining results derived from Inverted Index, Type Distribution and Document Vector, is significantly superior to the text-matching-based one.
Cites methods from "A Survey of Text Similarity Approac..."
...The vector similarity algorithm used in k-Means is cosine similarity(Gomaa and Fahmy, 2013)....
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Posted Content•
TL;DR: This article used techniques from authorship attribution and machine learning to determine whether comments from user accounts that are active in the Bitcoin block size debate are from the same author and achieved up to 72% for the true positive rate.
Abstract: The block size debate has been a contentious issue in the Bitcoin com- munity on the social media platform Reddit Many members of the com- munity suspect there have been organized attempts to manipulate the debate, from people using multiple accounts to over-represent and mis- represent some sides of the debate The following analysis uses techniques from authorship attribution and machine learning to determine whether comments from user accounts that are active in the debate are from the same author The techniques used are able to recall over 90% of all in- stances of multiple account use and achieve up to 72% for the true positive rate
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.
13,049 citations
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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