Entity Disambiguation in Natural Language Text Requirements

Misra, Janardan; Das, Subhabrata

doi:10.1109/apsec.2013.41

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…--Lexical Ambiguity: The main focus at this level is the ambiguity caused by words and terms. We recognized 6 papers related to this level [10], [93], [104]- [107]. --Syntactic Ambiguity: This level focuses on detecting sentences that have different possible grammatical structures.…”

Section: ) Requirements Extractionmentioning

confidence: 99%

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

2022

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Natural Language Processing (NLP) is widely used to support the automation of different Requirements Engineering (RE) tasks. Most of the proposed approaches start with various NLP steps that analyze requirements statements, extract their linguistic information, and convert them to easy-toprocess representations, such as lists of features or embedding-based vector representations. These NLPbased representations are usually used at a later stage as inputs for machine learning techniques or rulebased methods. Thus, requirements representations play a major role in determining the accuracy of different approaches. In this paper, we conducted a survey in the form of a systematic literature mapping (classification) to find out (1) what are the representations used in RE tasks literature, (2) what is the main focus of these works, (3) what are the main research directions in this domain, and ( 4) what are the gaps and potential future directions. After compiling an initial pool of 2,227 papers, and applying a set of inclusion/exclusion criteria, we obtained a final pool containing 104 relevant papers. Our survey shows that the research direction has changed from the use of lexical and syntactic features to the use of advanced embedding techniques, especially in the last two years. Using advanced embedding representations has proved its effectiveness in most RE tasks (such as requirement analysis, extracting requirements from reviews and forums, and semantic-level quality tasks). However, representations that are based on lexical and syntactic features are still more appropriate for other RE tasks (such as modeling and syntax-level quality tasks) since they provide the required information for the rules and regular expressions used when handling these tasks. In addition, we identify four gaps in the existing literature, why they matter, and how future research can begin to address them.

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Section: ) Requirements Extractionmentioning

confidence: 99%

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

2022

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“…-Lexical Ambiguity: The main focus at this level is the ambiguity caused by words and terms. We recognized 6 papers related to this level [93,104,105,106,10,107].…”

Section: Quality Assessmentmentioning

confidence: 99%

“…Then, each requirement is represented as a vector representation in this k-dimensional space. In most related papers, this representation is employed to calculate the similarity between requirements as a part of similarity-based rules [70,107] or clustering machine learning techniques [135,99,78].…”

Section: Topic Modeling Based Representationsmentioning

confidence: 99%

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

Sonbol,

Rebdawi,

Ghneim

2022

Preprint

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Natural Language Processing (NLP) is widely used to support the automation of different Requirements Engineering (RE) tasks. Most of the proposed approaches start with various NLP steps that analyze requirements statements, extract their linguistic information, and convert them to easy-toprocess representations, such as lists of features or embedding-based vector representations. These NLP-based representations are usually used at a later stage as inputs for machine learning techniques or rule-based methods. Thus, requirements representations play a major role in determining the accuracy of different approaches. In this paper, we conducted a survey in the form of a systematic literature mapping (classification) to find out ( 1) what are the representations used in RE tasks literature, ( 2) what is the main focus of these works, (3) what are the main research directions in this domain, and ( 4) what are the gaps and potential future directions. After compiling an initial pool of 2,227 papers, and applying a set of inclusion/exclusion criteria, we obtained a final pool containing 104 relevant papers. Our survey shows that the research direction has changed from the use of lexical and syntactic features to the use of advanced embedding techniques, especially in the last two years. Using advanced embedding representations has proved its effectiveness in most RE tasks (such as requirement analysis, extracting requirements from reviews and forums, and semantic-level quality tasks). However, representations that are based on lexical and syntactic features are still more appropriate for other RE tasks (such as modeling and syntax-level quality tasks) since they provide the required information for the rules and regular expressions used when handling these tasks. In addition, we identify four gaps in the existing literature, why they matter, and how future research can begin to address them.

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“…Requirements are assumed to be free from linguistic errors as well as terminological ambiguities [25], else quality of the final outputs may be lower, which for example, depends upon the identification of entity and action terms. It is further assumed that requirements correctly express the intention of its writer else errors in the estimation of semantic associations among requirements might increase.…”

Section: Inputsmentioning

confidence: 99%

Latent semantic centrality based automated requirements prioritization

Misra¹,

Sengupta²,

Das³

2014

Proceedings of the 7th India Software Engineering Conference

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This paper focuses on the problem of assigning relative priorities to requirements specified in the natural language. Proposed method includes processing plain text requirements specifications in order to extract multidimensional statistical features from the given requirement text to estimate latent semantic cohesion among the requirements as well as specific information contained within requirements. Using these estimates, latent semantic centrality and relative information specificity scores are derived for each requirement, which form the basis for assigning relative priorities to the requirements. We next extend this approach to clusters of requirements, where in requirements are clustered into semantically coherent groups and relative priorities are assigned to these groups of requirements. We further discuss a method for identifying semantically isolated requirements which might demand further analysis and elaboration by user. Suggested approach is specifically amenable to automated tool support as the case of prototype tool used in experiments to assess feasibility and effectiveness of the proposed approach demonstrates.

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Entity Disambiguation in Natural Language Text Requirements

Cited by 7 publications

References 20 publications

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

Latent semantic centrality based automated requirements prioritization

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