5 research outputs found
A Comparative Review of Machine Learning for Arabic Named Entity Recognition
Arabic Named Entity Recognition (ANER) systems aim to identify and classify Arabic Named entities (NEs) within Arabic text. Other important tasks in Arabic Natural Language Processing (NLP) depends on ANER such as machine translation, question-answering, information extraction, etc. In general, ANER systems can be classified into three main approaches, namely, rule-based, machine-learning or hybrid systems. In this paper, we focus on research progress in machine-learning (ML) ANER and compare between linguistic resource, entity type, domain, method and performance. We also highlight the challenges when processing Arabic NEs through ML systems
Arabic Rule-Based Named Entity Recognition Systems Progress and Challenges
Rule-based approaches are using human-made rules to extract Named Entities (NEs), it is one of the most famous ways to extract NE as well as Machine Learning. Â The term Named Entity Recognition (NER) is defined as a task determined to indicate personal names, locations, organizations and many other entities. In Arabic language, Big Data challenges make Arabic NER develops rapidly and extracts useful information from texts. The current paper sheds some light on research progress in rule-based via a diagnostic comparison among linguistic resource, entity type, domain, and performance. We also highlight the challenges of the processing Arabic NEs through rule-based systems. It is expected that good performance of NER will be effective to other modern fields like semantic web searching, question answering, machine translation, information retrieval, and abstracting systems
Development of an Arabic conversational intelligent tutoring system for education of children with autism spectrum disorder
Children with Autism Spectrum Disorder (ASD) are affected in different degrees in
terms of their level of intellectual ability. Some people with Asperger syndrome or
high functioning autism are very intelligent academically but they still have
difficulties in social and communication skills. In recent years, many of these pupils
are taught within mainstream schools. However, the process of facilitating their
learning and participation remains a complex and poorly understood area of education.
Although many teachers in mainstream schools are firmly committed to the principles
of inclusive education, they do not feel that they have the necessary training and
support to provide adequately for pupils with ASD. One solution for this problem is
to use a virtual tutor to supplement the education of pupils with ASD in mainstream
schools. This thesis describes research to develop a Novel Arabic Conversational
Intelligent Tutoring System (CITS), called LANA, for children with ASD, which
delivers topics related to the science subject by engaging with the user in Arabic
language. The Visual, Auditory, and Kinaesthetic (VAK) learning style model is used
in LANA to adapt to the children’s learning style by personalising the tutoring session.
Development of an Arabic Conversational Agent has many challenges. Part of the
challenge in building such a system is the requirement to deal with the grammatical
features and the morphological nature of the Arabic language. The proposed novel
architecture for LANA uses both pattern matching (PM) and a new Arabic short text
similarity (STS) measure to extract facts from user’s responses to match rules in
scripted conversation in a particular domain (Science). In this research, two prototypes
of an Arabic CITS were developed (LANA-I) and (LANA-II). LANA-I was developed
and evaluated with 24 neurotypical children to evaluate the effectiveness and
robustness of the system engine. LANA-II was developed to enhance LANA-I by
addressing spelling mistakes and words variation with prefix and suffix. Also in
LANA-II, TEACCH method was added to the user interface to adapt the tutorial
environment to the autistic students learning, and the knowledge base was expanded
by adding a new tutorial. An evaluation methodology and experiment were designed
to evaluate the enhanced components of LANA-II architecture. The results illustrated
a statistically significant impact on the effectiveness of LANA-II engine when
compared to LANA-I. In addition, the results indicated a statistically significant
improvement on the autistic students learning gain with adapting to their learning
styles indicating that LANA-II can be adapted to autistic children’s learning styles and
enhance their learning