Anaphora Resolution in Business Process Requirement Engineering

Anaphora resolution (AR) is one of the most important tasks in natural language processing which focuses on the problem of resolving what a pronoun, or a noun phrase refers to. Moreover, AR plays an essential role when dealing with business process textual description, either when trying to discover the process model from the text, or when validating an existing model. It helps these systems in discovering the core components in any process model (actors and objects).In this paper, we propose a domain specific AR system. The approach starts by automatically generating the concept map of the text, then the system uses this map to resolve references using the syntactic and semantic relations in the concept map. The approach outperforms the state-of-the art performance in the domain of business process texts with more than 73% accuracy. In addition, this approach could be easily adopted to resolve references in other domains

Arabic tweeps dialect prediction based on machine learning approach

In this paper, we present our approach for profiling Arabic authors on twitter, based on their tweets. We consider here the dialect of an Arabic author as an important trait to be predicted. For this purpose, many indicators, feature vectors and machine learning-based classifiers were implemented. The results of these classifiers were compared to find out the best dialect prediction model. The best dialect prediction model was obtained using random forest classifier with full forms and their stems as feature vector

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

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-to-process 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

On Ontological Expressivity and Modelling Argumentation Schemes Using COGUI

International audienceKnowledge elicitation, representation and reasoning explanation by / to non computing experts has always been considered as a crafty task due to difficulty of expressing logical statements by non logicians. In this paper, we use the COGUI editor in order to elicit and represent Argumentation Schemes within an inconsis-tent knowledge base. COGUI is a visual, graph based knowledge representation editor compatible with main Semantic Web languages. COGUI allows for default reasoning on top of ontologies. We investigate its use for modelling and reasoning using Argumentation Schemes and discuss the advantages of such representation. We show how this approach can be useful in the practical setting of EcoBioCap where the different Argumentation Schemes can be used to lead reasoning