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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
21 May 2018
TL;DR: It was showed that the identified impacts of assuring virtual PLC (V-PLC) elements using traceability information can be reused to ensure a number of other PLCs or requirements in the systems models.
Abstract: In complex industrial projects, textual information has been recognized as an important factor for automatically recovering trace links in software development. The goal of this paper is to empirically investigate if the trace links in the simulation result can assist in validating a virtual Programmable Logic Controller (PLC) in the context of System Modeling Language (SysML). We integrate the concept of obstacle analysis to recover situations in which a safety requirement will not be satisfied. Therefore, we use fault tree analysis to validate the safety requirements, and further use the elements of the fault tree to evaluate the quality of the automatically recovered trace links. We showed that the identified impacts of assuring virtual PLC (V-PLC) elements using traceability information can be reused to ensure a number of other PLCs or requirements in the systems models. This paper presents our experience of applying our approach using an automatic transmission systems built in SysML models.

7 citations

Proceedings ArticleDOI
13 Jun 2016
TL;DR: This work proposed a question model building with Bidirectional Long Short-Term Memory (BLSTM) neural networks, which as well can be used in other fields, such as sentence similarity computation, paraphrase detection, question answering and so on.
Abstract: Modeling sentence similarity all along is a challengeable task in the field of natural language processing (NLP), since ambiguity and variability of linguistic expression. Specifically, in the field of community question answering (CQA), homologous hotspot is focusing on question retrieval. To get the most similar question compared with user's query, we proposed a question model building with Bidirectional Long Short-Term Memory (BLSTM) neural networks, which as well can be used in other fields, such as sentence similarity computation, paraphrase detection, question answering and so on. We evaluated our model in labeled Yahoo! Answers data, and results show that our method achieves significant improvement over existing methods without using external resources, such as WordNet or parsers.

7 citations

Proceedings ArticleDOI
23 Aug 2014
TL;DR: A comparison of three methods for taxonomic-based sentence semantic relatedness, aided with word parts of speech (PoS) conversion using WordNet ontology and augmenting WordNet with two other lexicographical databases in assisting the word category conversion.
Abstract: In this paper, we present a comparison of three methods for taxonomic-based sentence semantic relatedness, aided with word parts of speech (PoS) conversion. We use WordNet ontology for determining word level semantic similarity while augmenting WordNet with two other lexicographical databases; namely Categorial Variation Database (CatVar) and Morphosemantic Database in assisting the word category conversion. Using a human annotated benchmark data set, all the three approaches achieved a high positive correlation reaching up to (r = 0.881647) with comparison to human ratings and two other baselines evaluated on the same benchmark data set.

6 citations


Additional excerpts

  • ...…question answering, automatic text scoring, plagiarism detection, machine translation, conversational agents among others (Ali, Ghosh, & Al-Mamun, 2009; Gomaa & Fahmy, 2013; Haque, Naskar, Way, Costa-Jussà, & Banchs, 2010; K. O’Shea, 2012; Osman, Salim, Binwahlan, Alteeb, & Abuobieda, 2012)....

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Journal ArticleDOI
TL;DR: In this article , a text analysis platform focused on the pharmaceutical domain is presented, which performs text classification using state-of-the-art transfer learning models based on spaCy, AllenNLP, BERT, and BioBERT.

6 citations

Journal ArticleDOI
TL;DR: The new representations constitute an alternative to the classic Cartesian-like 2-dimensional table and point towards advanced scientific visualization using present day computational resources.

6 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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