scholarly journals Predicting Verification Methods from Natural Language Requirements

2020 ◽  
Author(s):  
Michael Prendergast
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Semantic Analysis ◽  
Nearest Neighbor ◽  
Large Set ◽  
Technical Requirements ◽  
Verification Methods ◽  
Natural Language Requirements ◽  
Processing Algorithms

Abstract – A Verification Cross-Reference Matrix (VCRM) is a table that depicts the verification methods for requirements in a specification. Usually requirement labels are rows, available test methods are columns, and an “X” in a cell indicates usage of a verification method for that requirement. Verification methods include Demonstration, Inspection, Analysis and Test, and sometimes Certification, Similarity and/or Analogy. VCRMs enable acquirers and stakeholders to quickly understand how a product’s requirements will be tested.Maintaining consistency of very large VCRMs can be challenging, and inconsistent verification methods can result in a large set of uncoordinated “spaghetti tests”. Natural language processing algorithms that can identify similarities between requirements offer promise in addressing this challenge.This paper applies and compares compares four natural language processing algorithms to the problem of automatically populating VCRMs from natural language requirements: Naïve Bayesian inference, (b) Nearest Neighbor by weighted Dice similarity, (c) Nearest Neighbor with Latent Semantic Analysis similarity, and (d) an ensemble method combining the first three approaches. The VCRMs used for this study are for slot machine technical requirements derived from gaming regulations from the countries of Australia and New Zealand, the province of Nova Scotia (Canada), the state of Michigan (United States) and recommendations from the International Association of Gaming Regulators (IAGR).

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

Extractive Based Single Document Text Summarization Using Clustering Approach

2014 ◽  
Vol 3 (2) ◽  
pp. 73
Author(s):  
Pankaj Kailas Bhole ◽  
A. J. Agrawal
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Computational Intelligence ◽  
Clustering Algorithm ◽  
Text Summarization ◽  
Large Set ◽  
Meaningful Information ◽  
Time Reading ◽  
Clustering Approach

Text  summarization is  an  old challenge  in  text  mining  but  in  dire  need  of researcher’s attention in the areas of computational intelligence, machine learning  and  natural  language  processing. We extract a set of features from each sentence that helps identify its importance in the document. Every time reading full text is time consuming. Clustering approach is useful to decide which type of data present in document. In this paper we introduce the concept of k-mean clustering for natural language processing of text for word matching and in order to extract meaningful information from large set of offline documents, data mining document clustering algorithm are adopted.

scholarly journals IDL-Expressions: A Formalism for Representing and Parsing Finite Languages in Natural Language Processing

2004 ◽  
Vol 21 ◽  
pp. 287-317 ◽  
Author(s):  
M. J. Nederhof ◽  
G. Satta
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Upper Bound ◽  
Time Complexity ◽  
Large Set ◽  
New Formalism ◽  
Language Generation ◽  
Parsing Algorithm ◽  
Formal Properties

We propose a formalism for representation of finite languages, referred to as the class of IDL-expressions, which combines concepts that were only considered in isolation in existing formalisms. The suggested applications are in natural language processing, more specifically in surface natural language generation and in machine translation, where a sentence is obtained by first generating a large set of candidate sentences, represented in a compact way, and then by filtering such a set through a parser. We study several formal properties of IDL-expressions and compare this new formalism with more standard ones. We also present a novel parsing algorithm for IDL-expressions and prove a non-trivial upper bound on its time complexity.

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