Semantic similarity of short texts in languages with a deficient natural language processing support

Furlan, Bojan; Batanović, Vuk; Nikolić, Boško

doi:10.1016/j.dss.2013.02.002

Cited by 34 publications

(21 citation statements)

References 3 publications

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“…We then remove the row and the column of the matrix to which the selected cell belonged, thereby discarding all other word pairs in which words from the chosen pair appeared. We repeat this procedure until there are no more rows and/or columns left in the matrix [28]. Equation (1) show how the final similarity between two comments is calculated [28].…”

Section: B Clusteringmentioning

confidence: 99%

“…We repeat this procedure until there are no more rows and/or columns left in the matrix [28]. Equation (1) show how the final similarity between two comments is calculated [28].…”

Section: B Clusteringmentioning

confidence: 99%

“…The popular methods of measuring the similarity are divided into the three main categories based on the used criteria: common words, TF-IDF, and use of linguistic criteria [27]. The similarity measure used in our approach is based on cosine similarity and semantic and lexical distance between sentences [28]. In this similarity measure, to measure the similarity of a word pair, first a string similarity is determined, and then the semantic one.…”

Section: B Clusteringmentioning

confidence: 99%

“…Thus, words appearing in both texts are treated as a word pair which consists of two identical words. After calculating their similarity scores, they are added up into a similarity sum S same , and then these words are discarded from further consideration [28]. The goal is to match words across the two texts according to their mutual similarity score.…”

Section: B Clusteringmentioning

confidence: 99%

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Text Summarization of Multi-Aspect Comments in Social Networks in Persian Language

Shahverdian¹,

Saneifar²

2017

ijacsa

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Abstract-Now-a-days, there are increasingly huge amount of user generated comments on the web. The user generated comments usually contains useful and essential information reflecting public's or customers' opinions. Since the information in the comments could be used for decision making, production or service improvement, and achieving user satisfaction, the systematic analysis of these comments is an essential need in so many domains including e-commerce, production, and social network analysis. However, the analysis of large volume of comments is a difficult and time-consuming task. Therefore, the need for a system which can convert this massive volume of comments to a useful and efficient summary is felt more and more. Text summarization leads to using more resources at higher speeds and getting richer information. According to numerous studies conducted in the field of multi-document summarization, few studies can be found that have been focused on the user generated comments in Persian language. In this paper, we propose a novel approach to summarize huge amount of comments in Persian, which is enough close to a human summarization. Our approach is based on semantic and lexical similarities and uses a graph-based summarization. We also propose a clustering to deal with multiple aspects (subjects) in a corpus of comments. According to the experiments, the summaries extracted by the proposed approach reached an average score of 8.75 out of 10, which improves the state-of-theart summarizer's score about 14 percent.

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Section: B Clusteringmentioning

confidence: 99%

“…We repeat this procedure until there are no more rows and/or columns left in the matrix [28]. Equation (1) show how the final similarity between two comments is calculated [28].…”

Section: B Clusteringmentioning

confidence: 99%

Section: B Clusteringmentioning

confidence: 99%

Section: B Clusteringmentioning

confidence: 99%

See 2 more Smart Citations

Text Summarization of Multi-Aspect Comments in Social Networks in Persian Language

Shahverdian¹,

Saneifar²

2017

ijacsa

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show abstract

“…In this work, we build English word network. In a wider perspective of the information system, the methods of constructing and studying the lexical network of words are also important in the quantitative analysis of text mining, sentiment analysis, and corpus-based linguistic tools for information systems modeling, and applications of natural language processing [53][54][55].…”

Section: Literature Reviewmentioning

confidence: 99%

Quantitative learning strategies based on word networks

Zhao

Jia

Tang

et al. 2018

Physica A: Statistical Mechanics and its Applications

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h i g h l i g h t s• Quantitative English words learning strategies based on network and usage are proposed.• English words network combining etymology and usage information is constructed and analyzed.• The results show the strategies has significant improvement in learning efficiency and effectiveness. Keywords:Word network Network analysis Quantitative linguistics Vocabulary building Corpus-based linguistic analysis Learning English requires a considerable effort, but the way that vocabulary is introduced in textbooks is not optimized for learning efficiency. With the increasing population of English learners, learning process optimization will have significant impact and improvement towards English learning and teaching. The recent developments of big data analysis and complex network science provide additional opportunities to design and further investigate the strategies in English learning. In this paper, quantitative English learning strategies based on word network and word usage information are proposed. The strategies integrate the words frequency with topological structural information. By analyzing the influence of connected learned words, the learning weights for the unlearned words and dynamically updating of the network are studied and analyzed. The results suggest that quantitative strategies significantly improve learning efficiency while maintaining effectiveness. Especially, the optimized-weight-first strategy and segmented strategies outperform other strategies. The results provide opportunities for researchers and practitioners to reconsider the way of English teaching and designing vocabularies quantitatively by balancing the efficiency and learning costs based on the word network.

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Indexing of Textual Databases Based on Lexical Resources: A Case Study for Serbian

Stanković

Krstev

Obradović

et al. 2015

Semantic Keyword-Based Search on Structured Data Sources

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Semantic similarity of short texts in languages with a deficient natural language processing support

Cited by 34 publications

References 3 publications

Text Summarization of Multi-Aspect Comments in Social Networks in Persian Language

Text Summarization of Multi-Aspect Comments in Social Networks in Persian Language

Quantitative learning strategies based on word networks

Indexing of Textual Databases Based on Lexical Resources: A Case Study for Serbian

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