A Review of Natural Language Processing Techniques: Application to Afan Oromo

Language is a means of communication and a symbol of national identity. Afan Oromo is one of written and spoken indigenous language in Ethiopia which uses a writing system called Qubee. Natural language processing is automatic or semi-automatic processing of human language that helps computers to understand and process language. NLP techniques involve various linguistic levels to understand and use language. Linguistic levels are an explanatory method for presenting what actually happens within a natural language processing system. This is very important to develop appropriate and desired NLP applications at both higher and lower levels. In this paper, we present a review of techniques, current trends and challenges in NLP application to Afan Oromo.

Research Collaboration and Authorship Pattern in the field of Semantic Digital Libraries

This study aims to explore research collaborations and authorship patterns in the field of semantic digital libraries(SDL). The data is extracted (N=2075) from the Scopus database using keywords related to semantic digital libraries by considering all types of publications during 1983-2019. The analysis of each document is based on the following scientometrics indicators: author productivity, degree of collaboration, collaboration index, collaboration coefficient and modified collaboration coefficient. Correlation matrices were also calculated and inferences drawn in terms of authors and publications. A network visualisation tool VOSviewer was used to present authorship correlation network strength and keyword mapping for a better insight into the emerging areas in the field of SDL. The resulting average degree of collaboration of 0.898 indicates that a large number of publications are multi-authored and that there is a higher level of collaborative research in the field of semantic digital libraries. Meghini C from the Institute of Information Science and Technologies, Italy has produced the highest number of research paper(n=18) whereas Egenhofer MJ found to be a profoundly impacted author with 851 citations on in the studied domain. Results also reveal that the focus areas of research related to SDL include digital libraries, semantic web, ontology, metadata and information retrieval. However, keywords such as natural language processing system, computational linguistics, linked data are also repeated frequently in the published literature, thus revealing the emerging areas of the future research in the domain of SDL.

An Innovative AI-Based System for Corruption Risks Assessment Among Corporate Managers to Support Open Source Analysis

The paper has its focus on the creation of an innovative Natural Language Processing system for the quest of available information and consequent data analysis, aimed at reconstructing the corporate chain and monitoring the sensitive risk of corruption for people involved in command positions. Today, the greatest opportunity in finding information is represented by the Internet or other open sources, where the contents related to corporate managers are continuously posted and updated. Given the vastness of the information dimension, it seems remarkably advantageous to have an intelligent analysis system capable of independently finding, analyzing and synthesizing information related to a set of target subjects. The aim of this document is to describe a forecasting model based on Machine Learning and Artificial Intelligence techniques capable of understanding whether a news item related to an individual (sought during a due diligence process) contains information about crime, investigation, conviction, fraud, corruption or sanction relating to the subject sought. Methods based on Artificial Neural Networks and Support Vector Machine, compared one to the others, are introduced and applied for the scope. In particular, results showed the architecture based on SVM with TF-IDF matrix and test pre-processing outperforms the others discussed in this paper demonstrating high accuracy and precision in prediction new data as well.

An Integrated Process Based Natural Language Processing System

Natural Language Processing (NLP) systems involve Natural Language Understanding (NLU), Dialogue Management (DM) and Natural Language Generation (NLG). The purpose of this work involves integrating learning with examples and rule-based processing to design an NLP system. The design involves a three-stage processing framework, which combines syntactic generation, semantic extraction and a strong rule-based control. The syntactic generator generates syntax by aligning sentences with Part-of-Speech (POS) tags limited by the number of words in the lexicon. The semantic extractor extracts meaningful keywords from the queries raised. The above two modules are controlled by generalized rules by the rule-based controller module. The system is evaluated under different domains. The results reveal that the accuracy of the system is 92.33% on an average. The design process is simple, and the processing time is 2.12 seconds, which is minimal compared to similar statistical models. The performance of an NLP tool in a certain task can be estimated by the quality of its predictions on the classification of unseen data. The results reveal similar performance with existing systems indicating the possibility of usage for similar tasks. The system supports a vocabulary of about 700 words and can be used as an NLP module in a spoken dialogue system for various domains or task areas.