Assistant Professor
Jordan University of Science and Technology, Computer Science
Dr. Mohammad AL-Smadi (PhD) is an Assistant Professor at Jordan University of Science and Technology (JUST), Jordan. Before joining JUST in February 2014 he was a postdoctoral researcher at the Center for Educational Technology, Tallinn University. Dr. AL-Smadi holds a Doctoral degree in Engineering Studies (Computer Science) form Graz University of Technology with distinction, 2012. His research interests include Human-Computer Interaction, Technology Enhanced Learning, Social and Semantic Computing, and Information Search and Retrieval. Dr. AL-Smadi has participated in EC-Funded Projects and in some other national and internal research projects, e.g. the Learning Layers project –large scale project - applying his experience in the R&D of the Social Semantic Services to provide Meaningful Learning at workplace. He has published over 45 scientific publications in peer-reviewed journals, and conferences and earned an outstanding paper award (CAA2012 conference) and a best paper award (MIPRO2009 conference).
Jordan University of Science and Technology, Computer Science
Tallinn University, Centre for Educational Technology
Graz University of Technology, Institute for Information Technology and Computer Media
Ph.D. in Computer Science
Graz University of Technology, Austria
Master of Computer Science
The University of Jordan, Jordan
Bachelor of Computer Science
Al-Mustansriah University, Baghdad
Currently, I am leading with my colleagues an Arabic Text Mining lab (ATM@JUST). ATM research aims at advancing the research of Arabic NLP and Mining through cutting edge research and tools. ATM research interests include, Sentiment Analysis, Authorship Authentication, Paraphrasing and Semantic Similarity, Affective Text Computing, Named Entity Disambiguation, and Event Extraction. ATM is supporting the Arabic Track at SemEvaL2016: Task 5, Aspect based Sentiment Analysis.
During my Post-doc Research at the institute of Information technology, Tallinn University, I was mainly focusing on Social & Semantic Computing, Knowledge Evolution, and Informal Learning at the workplace. Particularly R&D of tools to augment knowledge evolution on both individual and collective levels. Moreover, I was exploring the field of Sense-making and visualisations and how visual interactions can guide knowledge evolution. I was also involved in a European Funded Project for Scaling Technology at the Work place Learning Layers – see https://learning-layers.eu/.
If you are interested to join us please don't hesitate to contact us.
Sentiment Analysis (SA) is concerned with extracting the sentiments conveyed in a piece of text. Employing it to analyze the text comments people post on social media websites can be a more accurate measure of the public’s opinion than public polls. This is of great benefit to many parties including governments, corporations, etc. Despite the large interest in SA, the field is still young as most of the existing works focus on document-level or sentence-level SA. Recently, more effort and time have been dedicated to the more useful and more challenging aspect-based SA (ABSA). ABSA is concerned with analyzing a text (review/comment) written by human and extracting the possibly different sentiments the text convey about different aspects of different entities. This is a very challenging problem. Moreover, the limited existing works focus almost exclusively on English text. To the best of our knowledge, no prior work has addressed the ABSA of Arabic text. This work aims to take the first step into this difficult unchartered domain.
The rapid increase in digital information has raised great challenges especially when it comes to automated content analysis. The adoption of social media as a communication channel for political views demands automated methods for posts’ tone analysis, sentiment analysis, and emotional affect. This project proposes a novel approach of using aspect-based sentiment analysis in evaluating Arabic news posts affect on readers.
An introduction to the problems of computing with human languages. Parsing. Semantic representations. Text generation. Lexicography. Discourse. Sublanguage studies. Applications to database interfaces and information retrieval.
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Architecture of expert systems, including the structure of knowledge bases and various knowledge representation methods, inference engines and reasoning techniques, and search and exploitation of domain-specific knowledge through heuristic knowledge acquisition. Discussion of expert system shells, and their capabilities and limitations. Projects in specific disciplines using available shells.
Semantic computing is rapidly evolving interdisciplinary research field, in which computer content is structured and organized in a way to better match the user needs and intentions, thus to create more meaningful user experience. The course will provide some insights for the domain of semantic computing enabling the students to understand the aspects related to research, technologies and application domains of the field. The students will use their experience form other elate fields such as natural language analysis, machine learning, intelligent systems, social computing, multimedia semantics, software engineering, and many others to build new experiences related to the field semantic computing in different disciplines. The course covers different topics related to semantic computing based on introduction to the concepts, technology, applications and future of semantic computing. Topics to be covered are mainly categorized into: semantic analysis, semantic languages and integration, semantic applications, and semantic programming and interfaces. More specific topics - such as Tagging and Tag analysis, Linked Data, Semantic Repositories and Semantic Information Retrieval, Ontologies and Semantic Web, Semantic Systems Engineering, and Application Domains are covered in this course.
User modeling is a cross-disciplinary research field that attempts to construct models of human behavior within a specific computer environment. The goal is not to imitate human behavior but to make the machine able to understand what the expectations, goals, knowledge, information needs, and desires of a user are in terms of a specific computing environment. Recommendations utilize the information stored in a user model. Simple examples of a recommendation system are e-commerce sites which make use of the user’s previous purchasing and browsing behavior to recommend new products or personalized news recommendation based on the user's previous news articles read. In this class the focus is on obtaining a general understanding of state of the art user modeling techniques and recommendation mechanisms. The student will learn to critically discuss relevant topics and apply the mechanisms to different domains. They will do a course project where groups of students virtually design a system that explicitly models the user and provide recommendations in a domain chosen by the group. Students will learn about the techniques through presentations; reading/discussing seminal papers in the user modeling and recommendation literature and interactive experiments during the lecture hours. Each student will either write a survey of one chosen topic that relates to the student's interest/background or an implementation of a simple system (eg. mobile application, design prototype).
This course presents the fundamental concepts of Multimedia, Text, Audio, Video, Animation.
By studying this course students will be taught how to write a proposal, find related material (in the library, on the web, etc), present their work in front of audience, defend their work, know art of writing scientific papers, etc.
SemEval 2016 Task 5 - Aspect Based Sentiment Analysis (ABSA)
The SemEval ABSA task for 2016 (SE-ABSA16) gives the opportunity to participants to experiment with sentence-level ABSA -as in SE-ABSA15 (https://alt.qcri.org/semeval2015/task12/)-, and/or with text-level ABSA (new subtask). The task provides training and testing datasets for several domains in 8 languages. For each domain (e.g. restaurants) a common set of annotation guidelines is used across all languages. SE-ABSA16 offers 3 subtasks which are described below. Participating teams are free to submit runs (system outputs) for the subtasks, slots, domains and languages of their choice.
Track 11: Digital Living including E-Learning, E-Government, E-Health
13th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2016)
Nov 29th to Dec 2nd, 2016 - Royal Atlas and Spa, Agadir, Morocco
Track Chairs:
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I would be happy to talk to you if you need my assistance in your research or whether you need bussiness administration support for your company.
You can find me at my office located at Jordan University of Science and Technology, Engineering Building, C5-L2.
I am at my office every day, but you may consider a call to fix an appointment.