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Journal ArticleDOI

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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Citations
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Book ChapterDOI
04 Sep 2019
TL;DR: AEAT has been able to provide for first time a solution for identification in real time with enormous throughput that fulfil its needs, based in a combination of six algorithms based in three different ideas, n-gram, TI-ILF, and Monge-Elkan that has surpassed any previous expectative.
Abstract: In modern societies control is based on information. Nowadays, in many countries, companies are obligated to provide to tax administrations all their invoices and withholders and financial entities to provide information that is used to offer prefilled tax declaration. In the case of Spain, the Tax Agency (AEAT) receives 180 million invoices by month and must process in a few days at the end of January more than 500 millions of registers to prefill Income Tax forms. Hundreds of thousands of these data are not correctly identified by the provider and must be returned to the sender or stored as not identified and analyzed afterwards. Traditionally this process consumed many technical and human resources. AEAT has been able to provide for first time a solution for identification in real time with enormous throughput that fulfil its needs. It is based in a combination of six algorithms, based in three different ideas, n-gram, TI-ILF, and Monge-Elkan that has surpassed any previous expectative.

1 citations

Proceedings ArticleDOI
01 Sep 2017
TL;DR: A method through which one can numerically measure and quantify the scrappiness level of a website and also visually display this level is presented.
Abstract: Scraper sites are questionable quality sites that copy their content partially or entirely from other websites and sometimes gain more ranking and popularity to the detriment of the original websites. This usually happens from a search engine point of view. Misleading a user to a scraper site almost always implies an unhappy, time consuming user experience, the scraper site being an unnecessary link in the user's navigation path. In this paper we present a method through which one can numerically measure and quantify the scrappiness level of a website and also visually display this level. In the same time, this paper wants to advert to the web and research communities about this type of websites and to urge actions against them.

1 citations


Cites background from "A Survey of Text Similarity Approac..."

  • ...Future evaluation of different text similarity functions such as character based similarity functions or term based similarity functions [21] should be performed in order to check how they perform and fit in this scenario....

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Book ChapterDOI
16 Jun 2021
TL;DR: In this paper, a text-mining approach is used to find identical products in the offer of different retailers, assuming that the data may contain incomplete information, and the results for real-world data fetched from the offers of two consumer electronics retailers are demonstrated.
Abstract: In recent years Machine Learning and Artificial Intelligence are reshaping the landscape of e-commerce and retail. Using advanced analytics, behavioral modeling, and inference, representatives of these industries can leverage collected data and increase their market performance. To perform assortment optimization – one of the most fundamentals problems in retail – one has to identify products that are present in the competitors’ portfolios. It is not possible without effective product matching. The paper deals with finding identical products in the offer of different retailers. The task is performed using a text-mining approach, assuming that the data may contain incomplete information. Besides the description of the algorithm, the results for real-world data fetched from the offers of two consumer electronics retailers are being demonstrated.

1 citations

Book ChapterDOI
10 Jul 2019
TL;DR: A framework for calculating the similarity of Chinese addresses is constructed, taking into account the hierarchical nature of addresses, and has achieved good results in practice for financial anti-fraud tasks.
Abstract: How to quickly measure the similarity of addresses has become an urgent need in various fields including financial anti-fraud. Traditional string-based similarity calculation methods have not completed this task perfectly. Taking into account the hierarchical nature of addresses, we constructed a framework for calculating the similarity of Chinese addresses. First, the whole address strings are split and annotated with proper level by a LM-LSTM-CRF model, and then sub-string level similarities are calculated. Last, similarity scores are combining by BP neural networks. This framework has achieved good results in practice for financial anti-fraud tasks.

1 citations

References
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Journal ArticleDOI
01 Sep 2000-Language
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

Journal ArticleDOI
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

Journal ArticleDOI
01 Jul 1945-Ecology

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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Journal ArticleDOI
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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Journal ArticleDOI
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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