Mining Text Patterns for Synonyms Extraction

Simanovsky, Andrey; Ulanov, Alexander

doi:10.1109/dexa.2011.53

Cited by 13 publications

(8 citation statements)

References 7 publications

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“…In order to objectively evaluate our approaches, the SET was implemented, with three typical synonym extraction approaches integrated into SET: (1) an approach based on distributional similarity [6]; (2) an approach based on lexical pattern [11] and (3) a knowledge system–based approach [19]. The precision of the three synonym extraction approaches evaluated by us was found to be slightly lower than those reported in the relevant papers.…”

Section: Methodsmentioning

confidence: 99%

“…Dias et al [18] proposed an unsupervised methodology that automatically discovers pairs of semantically related words by highlighting their local environment and evaluating their semantic similarity in local and global semantic spaces. Simanovsky and Ulanov [19] addressed the issue of patterns in synonym extraction by exploring the article redirects and anchor-text links in Wikipedia according to a particular confidence level.…”

Section: Related Workmentioning

confidence: 99%

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Automatically refining synonym extraction results: Cleaning and ranking

Liu

2018

Journal of Information Science

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Synonyms are crucial resources for many semantic applications, and the issue of synonym extraction has been studied extensively. However, extraction accuracy still cannot meet the practical demands. In addition, manually refining extraction results is time consuming. This article focuses on refining synonym extraction results by cleaning and ranking. A new graph model, the synonym graph, is proposed for the purpose of transforming the synonym extraction result of each word into a directed graph. Following this, two approaches for refining synonym extraction results are proposed based on the synonym graph. The first approach divides each extraction result into two parts – synonyms and noise – and detects noise by analysing the connectivity of the synonym graph. The second approach ranks the words in each extraction result by computing their semantic distance in the synonym graph. This approach was found to be more flexible than the first. The results of the experiments conducted in this study indicate that the performance of both of our proposed approaches is effective. In particular, they were found to perform well with datasets containing large synonym extraction results, which is important to reducing the cost of refining.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Automatically refining synonym extraction results: Cleaning and ranking

Liu

2018

Journal of Information Science

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“…We found two frames in which this problem has been considered: text mining and multi-database mining. In text mining, [12] aims to extract synonym terms from a text collection. The proposed approach relies on the identification of common synonym patterns, i.e.…”

Section: Item Similarity Miningmentioning

confidence: 99%

Handling Item Similarity in Behavioral Patterns through General Pattern Mining

Daher

Brun

2020

2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

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Modeling human behavior on the Web is often performed by sequential pattern mining (SPM). However, the similarity between data elements often results in the decrease of the number of patterns mined. This work proposes to handle this similarity by managing multiple data sources representing different views of the data. We introduce G SPM, a behavioral pattern mining algorithm that takes advantage of multi-source data to handle the problem of data similarity. It adopts a selective mining strategy to limit the complexity and forms general patterns to limit the decrease of the patterns.Experimental results confirm that G SPM succeeds in handling the problem of item similarity. In addition, G SPM outperforms traditional approaches in terms of runtime and redundancy of the resulting set of patterns.

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“…Kawai et al (2012) proposed a synonym extraction method in specification document by considering the co-occurrence words of component words. Simanovsky and Ulanov (2011) proposed a pattern extraction algorithm to extract the text fragments between pairs of synonyms by exploiting on large scale repositories, namely Wikipedia. In order to apply pattern extraction to Wikipedia, this technique builds a set called 'synonymous phrases' or mark-up on Wikipedia articles by considering the redirect pages titles, anchor text of back-links and disambiguation pages titles.…”

Section: Related Workmentioning

confidence: 99%

Automatic discovery and ranking of synonyms for search keywords in the web

Srikantaiah

Roopa

Kumar

et al. 2014

IJWS

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Search engines are an indispensable part of a web user's life. A vast majority of these web users experience difficulties caused by the keyword-based search engines such as inaccurate results for queries and irrelevant URLs even though the given keyword is present in them. Also, relevant URLs may be lost as they may have the synonym of the keyword and not the original one. This condition is known as the polysemy problem. To alleviate these problems, we propose an algorithm called automatic discovery and ranking of synonyms for search keywords in the web (ADRS). The proposed method generates a list of candidate synonyms for individual keywords by employing the relevance factor of the URLs associated with the synonyms. Then, ranking of these candidate synonyms is done using co-occurrence frequencies and various page count-based measures. One of the major advantages of our algorithm is that it is highly scalable which makes it applicable to online data on the dynamic, domain-independent and unstructured World Wide Web. The experimental results show that the best results are obtained using the proposed algorithm with WebJaccard.

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Mining Text Patterns for Synonyms Extraction

Cited by 13 publications

References 7 publications

Automatically refining synonym extraction results: Cleaning and ranking

Automatically refining synonym extraction results: Cleaning and ranking

Handling Item Similarity in Behavioral Patterns through General Pattern Mining

Automatic discovery and ranking of synonyms for search keywords in the web

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