Sentence Similarity Computation based on WordNet and VerbNet

Wali, Wafa; Gargouri, Bilel; Hamadou, Abdelmajid Ben

doi:10.13053/cys-21-4-2853

Cited by 6 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wafa Wali et al [4] proposed several methods for calculating the semantic similarity among two English sentences, which consider semantic and syntactic knowledge. It presented a technique for measuring sentence similarity, which combined the three components: lexical similarity, semantic similarity, and syntactic-semantic similarity.…”

Section: Related Workmentioning

confidence: 99%

“…Further, it creates job opportunities to a large scale of the population. This paper uses the an English approach based on latent semantic analysis [3], [4] for measuring the semantic similarity between English sentences of agricultural data and user query to find the appropriate solution for the complaints of farmers. The proposed system used SVM classification in MapReduce Hadoop environment to classify the agricultural dataset complaints based on crop name to improve the efficiency of the semantic similarity process.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

English Semantic Similarity based on Map Reduce Classification for Agricultural Complaints

Rslan¹,

Khafagy²,

Munir³

et al. 2021

IJACSA

View full text Add to dashboard Cite

Due to environmental changes, including global warming, climatic changes, ecological impact, and dangerous diseases like the Coronavirus epidemic. Since coronavirus is a hazardous disease that causes many deaths, government of Egypt undertook many strict regulations, including lockdowns and social distancing measures. These circumstances have affected agricultural experts' presence to help farmers or advise on solving agricultural problems. For helping this issue, this work focused on improving support for farmers on the major field crops in Egypt Retrieving solutions corresponding to farmer query. For our work, we have mainly focused on detecting the semantic similarity between large agriculture dataset and user queries using Latent Semantic Analysis (LSA) based on Term Frequency Weighting and Inverse Document Frequency (TF-IDF) method. In this research paper, we apply SVM MapReduce classifier as a framework for paralleling and distributing the work on the dataset to classify the dataset. Then we apply different approaches for computing the similarity of sentences. We presented a system based on semantic similarity methods and support vector machine algorithm to detect the similar complaints of the user query. Finally, we run different experiments to evaluate the performance and efficiency of the proposed system as the system performs approximately 77.8%~94.8% in F-score measure. The experimental results show that the accuracy of SVM classifier is approximately 88.68%~89.63% and noted the leverage of SVM classification to the semantic similarity measure between sentences.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

English Semantic Similarity based on Map Reduce Classification for Agricultural Complaints

Rslan¹,

Khafagy²,

Munir³

et al. 2021

IJACSA

View full text Add to dashboard Cite

show abstract

“…Wali et al [47] proposed the original idea because it has not been employed yet in former research in the literature. They presented a Hybrid Similarity measure that aggregated in linear function, three components (Lexical similarity using Lexsim, semantic similarity using Semsim that uses the synonymy words extracted from WordNet and syntacticsemantic similarity SynSenSim based on common semantic arguments such as thematic role and semantic class.)…”

Section: Literature Reviewmentioning

confidence: 99%

Textual Similarity Measurement Approaches: A Survey (1)

Abo-Elghit

Al-Zoghby

Hamza

2020

The Egyptian Journal of Language Engineering

View full text Add to dashboard Cite

Survey research is appropriate and necessary to address certain research question types. This paper aims to provide a general overview of the textual similarity in the literature. Measuring textual similarity tends to have an increasingly important turn in related topics like text classification, recovery of specific information from data, clustering, topic retrieval, subject tracking, question answering, essay grading, summarization, and the nowadays trending Conversational Agents (CA), which is a program deals with humans through natural language conversation. Finding the similarity between terms is the essential portion of textual similarity, then used as a major phase for sentence-level, paragraph-level, and script-level similarities. In particular, we concern with textual similarity in Arabic. In the Arabic Language, applying Natural language Processing (NLP) tasks are very challenging indeed as it has many characteristics, which are considered as confrontations. However, many approaches for measuring textual similarity have been presented for Arabic text reviewed and compared in this paper.

show abstract

“…Potthast et al [14] discussed the Cross-Language Plagiarism Detection of Arabic-English documents. First, the system translates the text by retrieving all the available translations of synonyms for a word from WordNet [15], then applying keyphrase extraction. Finally, a combination of monolingual is calculated (Cosine similarity, N-Gram, and longest common subsequence (LCS)) to return similar sentences.…”

Section: Introductionmentioning

confidence: 99%

AgroSupportAnalytics: big data recommender system for agricultural farmer complaints in Egypt

Rslan

Khafagy²,

Ali³

et al. 2023

IJECE

View full text Add to dashboard Cite

<span>The world’s agricultural needs are growing with the pace of increase in its population. Agricultural farmers play a vital role in our society by helping us in fulfilling our basic food needs. So, we need to support farmers to keep up their great work, even in difficult times such as the coronavirus disease (COVID-19) outbreak, which causes hard regulations like lockdowns, curfews, and social distancing procedures. In this article, we propose the development of a recommender system that assists in giving advice, support, and solutions for the farmers’ agricultural related complaints (or queries). The proposed system is based on the latent semantic analysis (LSA) approach to find the key semantic features of words used in agricultural complaints and their solutions. Further, it proposes to use the support vector machine (SVM) algorithm with Hadoop to classify the large agriculture dataset over Map/Reduce framework. The results show that a semantic-based classification system and ﬁltering methods can improve the recommender system. Our proposed system outperformed the existing interest recommendation models with an accuracy of 87%.</span>

show abstract

Sentence Similarity Computation based on WordNet and VerbNet

Cited by 6 publications

References 13 publications

English Semantic Similarity based on Map Reduce Classification for Agricultural Complaints

English Semantic Similarity based on Map Reduce Classification for Agricultural Complaints

Textual Similarity Measurement Approaches: A Survey (1)

AgroSupportAnalytics: big data recommender system for agricultural farmer complaints in Egypt

Contact Info

Product

Resources

About