Named Entity Recognition (NER) is an elementary tool for all application areas in Natural Language Processing (NLP) such as Automatic Summarization, Information Extraction, Information Retrieval, Text Mining, Machine Translation, Question Answering, and Genetics. NER is a task to discover and categorises the named entities (‘atomic elements’) in the text into predefined classes such as the names of persons, organizations, locations, terminologies of time, quantity and etc. Different languages may have different morphologies and thus involve dissimilar NER procedures. For example, an Arabic NER system cannot be practically used in processing Malay texts due to the different morphological features. The morphological features of every language are rich and complex and donates to the difficulties of implementing an actual method to develop the accurate NER system. In this paper, we review on three main techniques that commonly used to develop an NER system well-known as Rule-Based, Machine Learning, and Hybrid approach. This paper also highlights the features of each technique.

The usage of the Conceive-Design-Implement-Operate or C-D-I-O initiative is well documented within the context of engineering undergraduate programmes. To date, there are over 140 universities around the world which are part of the initiative, and most, if not all, of these universities have utilised the initiative as a framework within undergraduate curricula. This has been initially done for engineering undergraduate programmes, with other disciplines (outside of engineering) now choosing to implement this framework within its own context -due to its success in enhancing the overall student learning experience, at the engineering undergraduate level. There are, however, limited studies on how the framework is utilised within the context of a postgraduate programme. In particular, there are little to no studies on how this framework may be utilised to enhance the student learning experience within research-based postgraduate programmes e.g. MSc (by research) or PhD programmes. This paper aims to firstly review the available literature on how the framework influences postgraduate programmes globally. An attempt would also be made to discuss how previous studies have applied the framework to postgraduate education. To further narrate the application of the framework to graduate studies, a case study of a research-mode MSc programme will be explored. Specifically, how C-D-I-O influenced

The relation extraction of crime news can help the monitoring specialists to accelerate the crime investigation. However, constructing patterns or designing templates manually requires domain experts. Also the built patterns do not guarantee complete differentiation among different relation instances. The automatic detection of crime entities and relationship among entities can help the regulatory authorities to accelerate the crime investigation and decision support instead of being reliant on manual process. This study aims to increase the effectiveness of the extraction of crime entities and relationship among entities based on the determination of crime lingusitic pattern using Minimal Differentiator Expressions (MDEs) that represent the cases that will be used by the CBR classifier. The proposed extraction methods can help in compiling a highly accurate and machine-understandable crime knowledge bases which can support the regulatory authorities’ investigation. This paper conducted on our proposed MDEs algorithm for linguistic pattern reuse in CBR approaches.