Semantic analysis of natural language software requirement

Hassan, Taimoor; Hassan, Shoaib; Yar, Muhammad Asfand; Younas, Waleed

doi:10.1109/intech.2016.7845013

Cited by 12 publications

(6 citation statements)

References 5 publications

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“…Semantic Analysis is based on the meaning that is conveyed by the text. Understanding the meaning and interpreting the words are done here, along with the structural analysis of sentences [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection

Gupta

Wu²,

Arora

et al. 2021

Journal of Healthcare Engineering

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A cancer tumour consists of thousands of genetic mutations. Even after advancement in technology, the task of distinguishing genetic mutations, which act as driver for the growth of tumour with passengers (Neutral Genetic Mutations), is still being done manually. This is a time-consuming process where pathologists interpret every genetic mutation from the clinical evidence manually. These clinical shreds of evidence belong to a total of nine classes, but the criterion of classification is still unknown. The main aim of this research is to propose a multiclass classifier to classify the genetic mutations based on clinical evidence (i.e., the text description of these genetic mutations) using Natural Language Processing (NLP) techniques. The dataset for this research is taken from Kaggle and is provided by the Memorial Sloan Kettering Cancer Center (MSKCC). The world-class researchers and oncologists contribute the dataset. Three text transformation models, namely, CountVectorizer, TfidfVectorizer, and Word2Vec, are utilized for the conversion of text to a matrix of token counts. Three machine learning classification models, namely, Logistic Regression (LR), Random Forest (RF), and XGBoost (XGB), along with the Recurrent Neural Network (RNN) model of deep learning, are applied to the sparse matrix (keywords count representation) of text descriptions. The accuracy score of all the proposed classifiers is evaluated by using the confusion matrix. Finally, the empirical results show that the RNN model of deep learning has performed better than other proposed classifiers with the highest accuracy of 70%.

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Section: Methodsmentioning

confidence: 99%

Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection

Gupta

Wu²,

Arora

et al. 2021

Journal of Healthcare Engineering

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“…In 2016, Taimoor Hassan et al [28], this paper provided a solution to the lack of thorough semantic information for optimal semantic understanding. The approach is to discover the significance of representations of software requirements and then determine the effect of semantic analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

State of Art for Semantic Analysis of Natural Language Processing

Maulud

Zeebaree

Jacksi

et al. 2021

QAJ

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Semantic analysis is an essential feature of the NLP approach. It indicates, in the appropriate format, the context of a sentence or paragraph. Semantics is about language significance study. The vocabulary used conveys the importance of the subject because of the interrelationship between linguistic classes. In this article, semantic interpretation is carried out in the area of Natural Language Processing. The findings suggest that the best-achieved accuracy of checked papers and those who relied on the Sentiment Analysis approach and the prediction error is minimal.

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“…Taimoor Hassan, et al [96]. This paper presented a solution to the non-availability of semantic knowledge for improved semantic interpretation.…”

Section: Ontologymentioning

confidence: 99%

Review on Natural Language Processing Based on Different Techniques

Maulud

Ameen

Omar

et al. 2021

AJRCoS

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With the exponential growth of the information on the Internet, there is a high demand for making this information readable and processable by machines. For this purpose, there is a need for the Natural Language Processing (NLP) pipeline. Natural language analysis is a tool used by computers to grasp, perceive, and control human language. This paper discusses various techniques addressed by different researchers on NLP and compares their performance. The comparison among the reviewed researches illustrated that good accuracy levels haved been achieved. Adding to that, the researches that depended on the Sentiment Analysis and ontology methods achieved small prediction error. The syntactic analysis or parsing or syntax analysis is the third stage of the NLP as a conclusion to use NLP technology. This step aims to accurately mean or, from the text, you may state a dictionary meaning. Syntax analysis analyzes the meaning of the text in comparison with the formal grammatical rules.

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Semantic analysis of natural language software requirement

Cited by 12 publications

References 5 publications

Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection

Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection

State of Art for Semantic Analysis of Natural Language Processing

Review on Natural Language Processing Based on Different Techniques

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