[SOLVED] Knowledge Representation and Reasoning

[SOLVED] Knowledge Representation and Reasoning

Knowledge Representation and Reasoning

Assignment 2_KRR
Topic: knowledge representation in AI and Natural Language processing
Assignment marked out of: 100% Weight: 40% of overall grade
Number of Words: 5000-7000
Deliverables:
• Soft copy via your account at Blackboard. Only one student submits if it is a group project
• Share a folder on either Google Drive or Dropbox, including report, presentation and five significant
papers. The folder name should be in the following format:
o Individual: Student ID-Student First Name
o Group: Group-Student ID-Student First Name, Student ID-Student First Name
1. Overview
In this project assignment, you will demonstrate your understanding of knowledge representation in a
Webbased practical AI programming project. Choose only one of the following assignments. It might be a
group assignment with maximum of two students; a division of work must be clarified in the report.
You might use a Java servlet (preferably using NetBeans 7 with Java EE and TomCat “can be downloaded for
free from http://netbeans.org/downloads/”) for making your assignment we-based.
Assignment 1: Statistical parsing of Arabic for web user interface. [1] -level basic
Assignment 2: Preprocessing of Arabic text: tokenization & POS tagging. [2] – level basic
Assignment 3: Developing an Arabic Named Entity Recognition System. [3, 10, 11]-level basic
Assignment 4: Processing Arabic Questions using open Source tools [5][6]-level intermediate-to-advanced
Assignment 5: Implementing Recommender systems using data mining and knowledge discovery tools. [7]-
level intermediate-to-advanced
Assignment 6: Automatic document summarization. [8]- level intermediate-to-advanced
Assignment 7: Using text genres analysis to verify financial institution annual reports against authoritative
guidance-level intermediate-to-advanced
Assignment 8: Analysis of Modality in Text Utterances-level basic
Assignment 9: Deep Learning for Arabic Natural Language Processing [12]-level intermediate-toadvanced
Proposed Assignment: You could also propose your own project that is related to the Knowledge
Representation and Reasoning module, but it needs approval from me. Your proposal should follow that same
format used in this assignment brief.
You will present your project and demonstrate your work in a lab session. You will also hand in a report,
which includes the following:
1. An introduction with a general description of the problem domain, and the aspects you focus on.
2. A description of your solution, including a description of the algorithm you defined, any clever ideas
you came up with or borrowed, and so on.
3. A discussion of the performance of the system, the problems encountered, error analysis, etc.
4. Conclusion, including suggestions for future enhancements.The report should be in PDF format and between 12 and 22 pages in size, excluding references. You are
required to use ACL (2012) style (available for LaTeX and Word) in producing the PDF document. These
templates are available at: http://acl2012.org/call/sub01.asp
You might use https://www.sharelatex.com/ for Latex documents
2. Requirements
Students are expected to implement a java web application (preferably using NetBeans 7 with Java EE and
TomCat “can be downloaded for free from http://netbeans.org/downloads/”), if possible, responsible for
complex knowledge representation in AI application.
Pls. see the Marking Scheme Section which will give you an idea about the criteria for marking and their
weights.
Note that the code will need to be extended and revised by other developers, so make sure to include full
and clear comments and documentation.
Assignment Milestones
Milestone 1: Preparation
1. You have acquired all the required training and testing data.
2. You have installed the necessary software (Netbeans, Weka, Bikel Parser, among others)
3. You have run the application on a small sample of data, or created a small “Hello world” application.
Milestone 2: Development
1. You have developed the application with all functions and features.
2. All various components, functions, features and classes are integrated together in one single
application.
3. The program accepts all instances of the training data as input and gives the expected output.
Milestone 3: Testing and Evaluation Deadline:
1. Gold standard is created or acquired.
2. Continuous cycle of testing-development-testing until satisfactory results are gained. Error analysis of
results achieved will guide you to the points of improvements. You can refer to your trial and the
mythology you followed.
3. Testing results in terms of standard evaluation metrics are reported with error analysis. Try to compare
with state of the art research.
2.1 Assignment 1: Statistical parsing of Arabic for web user interface
This task consists of training a statistical parser for Arabic and porting it on a web interface allowing it to accept
user input and provide parse results.
1. Training a statistical parser for Arabic: Use the Bikel parser which is already tuned for Arabic. The
parser can be downloaded from http://www.cis.upenn.edu/~dbikel/software.html and the (Arabic
Treebank) training data from Software and Resources folder. You might use/compare with another parser
(e.g. Stanford Arabic Parser, http://nlp.stanford.edu/software/lex-parser.shtml)
2. Port the parser to the web using a java servlet: From a web server (using NetBeans and TomCat) you
should be able to send input to the parser and get output from it to be displayed back in the server.
3. Take user input and give parse output: user input is a free Arabic script text not tokenized, transliterated
or formatted in any way. See how you can format the raw text to get a successful parse from the parser.
Provide means for presenting the output sentence graphically.4. Test and Evaluate
2.2 Assignment 2: Preprocessing of Arabic text: tokenization & POS tagging
This task consists of using SVM machine learning in order to pre-process raw Arabic text and produced
tokenized and part-of-speech (POS) tagged analysis. You are recommended to use WEKA or RapidMiner for
this task. Refer to the paper Automatic Tagging of Arabic Text: From Raw Text to Base Phrase Chunks By
Mona Diab, Kadri Hacioglu and Daniel Jurafsky, Published in HLT-NAACL 04 or recent work by the first
author. You can download the tool described in this paper from Software and Tools folder:
1. Design classifiers for tokenization of Arabic text: Arabic words consists of clitics that need to be
separated in the tokenization task.
2. Design classifiers for POS-tagging of Arabic text: Each word should be assigned the right POS
category
3. Train on the Arabic Treebank: The model will be trained on the Arabic Treebank from Software and
Resources folder
4. Test and evaluate.
A demonstration of a similar system can be seen here: http://nlp.ldeo.columbia.edu/amira/
Tokenization Sample Input sentence
ركلة بول ساندور يرجملا مكحلا یحتسب ولم نستا وردناسیلا قبل من ةقطنملا داخل ھیسكى عرقلة رثا صحیحة جزاء
wlm yHtsb AlHkm Almjry sAndwr bwl rklp jzA’ SHyHp Avr Erqlp hyskY dAxl AlmnTqp mn qbl
AlysAndrw nstA.
Tokenization Sample Output sentence w lm yHtsb Al Hkm Al mjry sAndwr bwl rklp jzA’ SHyHp Avr Erqlp
hyskY dAxl Al mnTqp mn qbl AlysAndrw nstA .
POS-Tagging Sample Input sentence w lm yHtsb Al Hkm Al mjry sAndwr bwl rklp jzA’ SHyHp Avr Erqlp
hyskY dAxl Al mnTqp mn qbl AlysAndrw nstA .
POS-Tagging Sample Output sentence w/CC lm/RP yHtsb/VBP AlHkm/NN Almjry/JJ
sAndwr/NO_FUNC bwl/NNP rklp/NN jzA’/NN SHyHp/JJ Avr/IN Erqlp/NN hyskY/NO_FUNC dAxl/IN
AlmnTqp/NN mn/IN qbl/NN AlysAndrw/NNP nstA/NN ./PUNC
2.3 Assignment 3: Developing an Arabic Named Entity Recognition System.
In this assignment, you have to build a rule-based Named Entity Recognition system (RBNER) for Arabic,
which is capable of identifying one or more of ENAMEX categories (i.e. Person, Location and Organization
NEs), using GATE tool [9]. A RBNER system consists basically of a set of linguistic rules (i.e. grammars)
and a set of gazetteers (i.e. dictionaries/keyword lists). A linguistic rule may utilize NE Gazetteer(s) in its
structure to support and implement the rule efficiently. Then, you will need to evaluate the performance of the
rule-based NER system when applied on a standard dataset/corpus (i.e. ANERcorp 1 dataset). It is
recommended that you have a look at the following papers: [3, 10, 11].
❖ The system environment: GATE platform [9], which allows you to implement linguistic rules,
create/add gazetteers and evaluate the produced system.
❖ The NE gazetteers: You need to consider NE gazetteers in the structure of the new linguistic rules.
An example of gazetteers to be considered is ANERGazet2
1 , 2 Available to download on http://www1.ccls.columbia.edu/~ybenajiba/downloads.html❖ The linguistic rules: The rules need to be implemented in JAPE language. Read the GATE user
manual to learn about JAPE. Also, reading [3, 10, 11] might help too.
❖ System evaluation: The performance of the system, when applied on ANERcorp dataset, can be
evaluated using GATE built-in evaluation tool, so-called AnnotationDiff. The results should be in terms
of precision, recall and f-measure.
2.4 Assignment 4: Processing Arabic Questions using open Source tools
In this assignment you will use any open source tool such as QANUS (can be downloaded from
http://www.qanus.com/download/) or OpenEphyra (can be downloaded from
http://sourceforge.net/projects/openephyra/) or any other tool to develop Question Answering System. Refer
to the papers in reference [5] and [6] to know more about Question Answering Systems and related tasks and
tools. Then, you can use some standard set of questions (both English and Arabic) from
TREC(http://www.emi.ac.ma/bouzoubaa/download/) or CLEF
(http://www1.ccls.columbia.edu/~ybenajiba/downloads.html).
1. Processing of questions: Process the English questions using the open source tool and predict the
classes of the question. Processing of question involves word segmentation and POS tagging.
2. Modification of the source code: Modify the source code of the tool to process Arabic questions
3. Test and evaluate: Compare the performance of the tool for both English and Arabic question
sets.
2.5 Assignment 5: Implementing Recommender systems using data mining and knowledge discovery
tools
Recommender Systems are software tools and techniques providing suggestions for items to such as what
items to buy, what music to listen to, or what online news to read. A recommender system normally focuses
on to generate the recommendations of a specific type of item based on some recommendation technique. You
can find more about recommender system in [7]. In this assignment, you are required to perform the following
task for recommender system:
1. Recommender Algorithms: Compile and compare the at least four recommender algorithms.
2. Data set and Tools: Identify the data set for recommender system. You can use your Facebook/
LinkenIn/ Instagram friend list or list of books on Amazon, YouTube video lectures, online music store
or any other data of your choice. Select any data mining tool useful for recommender system such as
RecommendeLab, RapidMiner, KNIME, Weka .
3. Implementation of Recommender Algorithm: Implement the best algorithm described in Task 1
using tools of your choice from step 1. A sample implementation can be found in
[https://bib.irb.hr/datoteka/596976.rcomm2012_recommenders.pdf].
4. Results: Present the results and interesting patterns.
2.6 Assignment 6: Automatic document summarization
Document summarization is the technique of identifying and extracting important information from text
documents. The output of the document summarization is usually significantly smaller than original document
and is not longer than half of the original document under any circumstances. In this assignment you are
required to do the following task:
1. Summarization Algorithms: Discuss at least three document summarization technique.2. Implementation of summarization algorithm: Implement one of the document summarization
techniques using Perl, Java or Python. Optionally you can use automatic summarization tool such as
Mead [http://www.summarization.com/mead/]
3. Results: Rate the summarization of text produced by program/tool. Present the summarization results.
2.7 Assignment 7: Using text genres analysis to verify financial institution annual reports against
authoritative guidance
Recently, the government of the United Arab Emirates has made progress to encourage corporates to include
sustainability efforts within their annual reports. These annual reports should communicate the corporate
efforts on sustainability, environment, and social issues. These reports are made public, but it can take a lot of
efforts to do manual review by current and/or prospect shareholders. Automating the task of verifying annual
reports against authoritative guidance issued by the government can help investors task corporate efforts on
sustainability. An example government guidance is given here:
https://www.google.com/url?sa=t&source=web&rct=j&url=https://adxservices.adx.ae/WebServices/DataSer
vices/contentDownload.aspx%3Fdoc%3D1704806&ved=2ahUKEwjQ7Yem1-
PmAhVgGDQIHbIgAPkQFjAIegQIBRAB&usg=AOvVaw2Oor9oMHrRh9ZBTEG0CVhw
The goal of this project would be to analyze annual reports of the top 10 corporations in Abu Dhabi Stock
Exchange and through NLP and text genres analysis, students are asked to find text pieces within these 10
reports that match/confirm the authoritative guidance given in the above link. Example corporate bank:
https://www.adcb.com/about/sstanrprt/sustainability.aspx
Student will need to define a text bi-clustering method to align text segments of corporate annual reports on
sustainability to text segments within government guidance. Ad hoc text analysis may also be applied to find
text patterns in annual reports. For example, students can run regular expressions rules to find tokens/phrases
that are over-represented/under-represented in annual reports.
2.8 Assignment 8: Analysis of Modality in Text Utterances
Modality is a system for enabling speakers to express intentions and beliefs through text. Modality can be
manifested in different forms including modal auxiliary verbs (must, should, can …). See this resource for
introduction: https://en.wikipedia.org/wiki/Linguistic_modality
Modality has a number of categories (epistemic, deontic, and circumstantial). Epistemic is related to
knowledge of the speaker about necessity or possibility of something. Deontic is related to obligations, while
circumstantial is related to context. Expression of modality can expose a great deal of unclarity regarding the
underlying category. For instance, consider these uses of the modal verb ‘must’:
(1) Agatha must be the murderer. (expressing epistemic modality)
(2) Agatha must go to jail. (expressing deontic modality)
(3) Agatha must sneeze. (expressing circumstantial modality)
Source: https://en.wikipedia.org/wiki/Linguistic_modality
The goal of this project is to analyze text corpora, identify modal expressions, and then classify them to
categories (e.g. epistemic, deontic …etc.). This method to realize this goal is open-ended (you can use
machine learning or develop your own rule-based system.
Please note that this project does not focus on one verb (must)! You need to cover a substantial amount of
modal expressions.
Bonus: It would be great if you develop a system of analyzing modality in Arabic literature.2.9 Assignment 9: Deep learning for Arabic NLP
Deep learning technology such as, RNNs, GRUs, CNNs and LSTMs, are now hot topics for NLP. The focus
will be on machine learning and specifically ‘deep’ neural network approaches to the automated analysis of
natural language text. Topics will typically include representation learning for words (and possibly larger
linguistic units). Neural networks are one of the most powerful classes of Arabic Natural language processing
model, achieving state-of-the-art results on a wide range of benchmarks. A key aspect behind their success is
the ability to discover representations that can capture relevant underlying structure in the training data. Any
of the following topics can be used in this assignment:
• Named Entity Recognition
• Sentiment Analysis
• Machine Translation
• Language Generation and Multi-Document Summarization
• Text Classification and Categorization
• Word Sense Disambiguation using various types of Recurrent Neural Networks
• Part-of-Speech Tagging
• Semantic Parsing and Question Answering
• Paraphrase Detection
• Character Recognition
• Spell Checking
• Word Embedding
• Stemming
I am going to specify the first three tasks and you need only to choose one of them. You can do the same if
you decided to choose another task.
2.7.1 Named Entity Recognition
Recognizing proper names in a piece of given text is very important for many tasks, including information
retrieval, information extraction, summarization, and translation. In this task, you will build a deep
learningbased named entity recognizer. You should use a bi-directional LSTM (check the resources below).
This task is a sequence labeling task, where your model should label each token as either, Person, Location,
Organization or Other. See the image below. For example, “Mr. Samy is on his way to Cairo”. Your model
should label “Samy” as Person (PER) named entity, and label “Cairo” as Location (LOC) named entity. All
other tokens should be labeled as Other (O).
Web link to the Dataset •
CONLL2003 dataset.Web links to Resources
• Named entity recognition with bidirectional LSTM-CNNs
• Neural architectures for named entity recognition
• https://www.sciencedirect.com/science/article/pii/S0885230818301657
• https://medium.com/@rohit.sharma_7010/a-complete-tutorial-for-named-entityrecognitionandextraction-in-natural-language-processing-71322b6fb090
2.7.2 Sentiment Analysis
The sentiment of a piece of text is the tone or the impression of its author. For instance, “The weather is cool!”
has a positive sentiment, while “This movie is a waste of time!” has a negative sentiment. In this task, you
will build a deep learning-based sentiment analyzer. You should use an architecture suitable for handling
sequences (RNN, LSTM, etc.). Your model should take a sentence as input and classify it to either positive or
negative sentiment.
Web link to the Dataset
• IMDB sentiment dataset. It contains ~25K reviews with their labels.
Web links to Resources
● http://karpathy.github.io/2015/05/21/rnn-effectiveness/
● An LSTM approach to short text sentiment classification with word embeddings
● https://towardsdatascience.com/sentiment-analysis-using-lstm-step-by-step-50d074f09948
2.7.3 Machine Translation
Machine Translation is the task of translating a piece of text in one language to its equivalent in another
language. Your task is to build and Arabic-English machine translation model. Your model should be based
on the Sequence-to-Sequence architectures (See resources below). Your model should have an encoder and a
decoder network based on LSTM. Bonus: Use attention mechanism (see resources below).
Web link to the Dataset
• OpenSubtitles 2018 Arabic- English dataset. (You can use other datasets for other languages but
ArabicEnglish is preferred).
Web links to Resources
● Sequence to sequence learning with neural networks● Effective approaches to attention-based neural machine translation
● Neural machine translation by jointly learning to align and translate
● https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html
● https://machinelearningmastery.com/define-encoder-decoder-sequence-sequencemodelneuralmachine-translation-keras/
In this assignment I suggest to use the following tools and technologies:
1. Anaconda — Anaconda is a free and open source distribution of the Python and R programming
languages for data science and machine learning related applications, that aims to simplify package
management and deployment. You can download it from the link below according to your system
https://www.anaconda.com/download/
2. Spyder — Spyder is an open source cross-platform IDE for scientific programming in the Python
language. It comes installed with anaconda. If not, install it using anaconda navigator.
3. Tensorflow — TensorFlow is an open-source software library for dataflow programming across a
range of tasks. Download link — https://www.tensorflow.org/install/install_windows
4. Keras — Keras is an open source neural network library written in Python. Activate Tensorflow env
and install keras using ‘pip install keras’
3. Guidelines for Report
Below are guidelines on how to write-up your report for the final project (no more than 7,000 words limit). Of
course, for a short class project, all of the sections may not be relevant. However, you may use it as a general
guide in structuring your final report.
A “standard” experimental AI paper consists of the following sections:
1. Introduction
Motivate and abstractly describe the problem you are addressing and how you are addressing it. What is the
problem? Why is it important? What is your basic approach? A short discussion of how it fits into related work
in the area is also desirable. Summarize the basic results and conclusions that you will present.
2. Problem Definition and Algorithm
2.1 Task Definition
Precisely define the problem you are addressing (i.e. formally specify the inputs and outputs). Elaborate on
why this is an interesting and important problem. Include a simple specific example, providing the I/O
showing how the output is related to the input specifying the desired/achieved properties of the output
illustrating the basic terms used.
2.2 Algorithm Definition
Describe in reasonable detail the algorithm (rules) you are using to address this problem. A pseudo-code
description of the algorithm you are using is frequently useful. Trace through a concrete example, showing
how your algorithm processes this example. The example should be complex enough to illustrate all of the
important aspects of the problem but simple enough to be easily understood. If possible, an intuitively
meaningful example is better than one with meaningless symbols.
3. Experimental Evaluation
3.1 Methodology
What are criteria you are using to evaluate your method? What specific hypotheses does your experiment test?
Describe the experimental methodology that you used. What are the dependent and independent variables?
What is the training/test data that was used, and why is it realistic or interesting? Exactly what performance
data did you collect and how are you presenting and analyzing it? Comparisons to competing methods that
address the same problem are particularly useful.
3.2 ResultsPresent the quantitative results of your experiments. Graphical data presentation such as graphs and
histograms are frequently better than tables. What are the basic differences revealed in the data? Are they
statistically significant?
3.3 Discussion
Is your hypothesis supported? What conclusions do the results support about the strengths and weaknesses of
your method compared to other methods? How can the results, be explained in terms of the underlying
properties of the algorithm and/or the data.
4. Related Work
Answer the following questions for each piece of related work that addresses the same or a similar problem.
What is their problem and method? How is your problem and method different? Why is your problem and
method better?
5. Future Work
What are the major shortcomings of your current method? For each shortcoming, propose additions or
enhancements that would help overcome it.
6. Conclusion
Briefly summarize the important results and conclusions presented in the paper. What are the most important
points illustrated by your work? How will your results improve future research and applications in the area?
Bibliography & Citations
Be sure to include a standard, well-formatted, comprehensive bibliography with citations from the text referring
to previously published papers in the scientific literature that you utilized or are related to your work. Always
use a consistent citation style for your references. The standard style used around the university is the Harvard
Style. However, I will accept any other standard style (e.g. APA style) as long as it is used consistently.
Try to make your report EASY to read.
• Be sure to include an overview in the beginning, which outlines what the report will be describing, in a
section-by-section fashion.
• Include simple examples (or better, a single simple example throughout), to help illustrate the ideas.
• A picture is worth (at least) a thousand words. Use figures, flow-charts, graphs, whenever appropriate.
• The material should be structured, and flow. It should NOT be a core-dump of everything you happened
to read when you were looking at things related to X. Readers (read “the people who will assign your
grade!”) get annoyed by having to wade through irrelevant material.
• If you are giving a high-level description of an algorithm, be sure to explicitly state its input and output.
• Many algorithms have a flow of information, from one subroutine to another. Provide one or more figures,
to make the ideas clear.
• Also, proof-read your report. As a grader, I find it very irritating to read a report that has pages of easytofix
typos, illegible figures, missing citations, etc. And you really don’t want to irritate the person who is
assigning your grade…
• If you are describing a precise algorithm, you should give the actual formulas, using terms that are
welldefined, in the report.
• Your report should be self-contained. You are allowed to copy figures from other sources (if they are
properly credited). But if you do, be sure to define the terms that appear in that figure!
• Save trees – hand in a 2-sided version. And use section numbers, and page numbers!
The submission must accompany a CD containing your code; also include a tutorial, and a user manual which
will help the user to run the agent based system. An agreed dataset should be provided. Use your creativity
to make the submission better .4. Demonstration
The demonstration times for individual teams will be posted later in the semester. It is planned that the
demonstrations will take place around the submission deadline. Pls. make your appointment.
5. Academic Integrity
Copying or paraphrasing someone’s work (code included), or permitting your own work to be copied or
paraphrased, even if only in part, is not allowed, and would result in a disciplinary action according to the
university policy. Any resources or ideas borrowed from other sources should be explicitly referenced in text
and bibliographies.
6. Marking Scheme:
The grading will be broken down based on the following criteria:
Deliverable Criterion Max Actual
Total for Software
Report based on
quality of report
Introduction
and Literature
Review
• • • • •
Articulation of research
issue/problem
Coherence of the research
aim(s) and objectives
Relevance and importance
of the research issue
Criteria for the proposed
solution
Explanation of constraints
10%
• • • • •
Organisation and logical
sequence of the contents of
the dissertation
Comprehensive and correct
citation of references and/or
bibliography Appropriate
written style and use of
language General quality of
presentation
Supporting documents are
provided
10%
• •
Comprehensive, rigorous
and critical review of the
literature
Appropriateness of
theoretical concepts
employed
10%
Subtotal 30%Research
Methodology,
and data
Analysis,
interpretation
and discussion
• • • •
Relevant and effective
research methodology
Rigour of application of the
methods of investigation
Identification of a solution
and exploration of
alternatives
Development of an
application
25%
• • • •
Quality and depth of
research
Appropriateness of
methods of analysis Rigour
of application of the
methods of analysis
Reproducibility of results
25%
Subtotal 50%
Creativity, and
Conclusion(s)
• • •
Producing results close
to or exceeding those in
published research. If
there is no relevant
published research, then
this score will be used
for accuracy and
coverage sufficient to
support interpretation
and conclusion. Testing
in multiple conditions
10%
• Design and implementation
that demonstrates software
engineering skills and
completeness
• • • • •
Consistency of
conclusion(s) with research
objectives Consistency
of
conclusion(s) with findings
and discussion
Comprehensive of the
implications of the
conclusion(s)
Appropriateness of
recommendations on the
basis of the conclusion(s)
Value of the research and
makes a contribution to
knowledge and /or practice
10%
Subtotal 20%
Total 100%References
[1] Daniel M. Bikel. 2004. A Distributional Analysis of a Lexicalized Statistical Parsing Model. In the
proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP
2004)
[2] Mona Diab, Kadri Hacioglu and Daniel Jurafsky. 2004. Automatic Tagging of Arabic Text: From Raw
Text to Base Phrase Chunks. In HLT-NAACL 04.
[3] Abdallah, S., Shaalan, K., Shoaib, M., Integrating Rule-based System with Classification for Arabic
Named Entity Recognition, Lecture Notes in Computer Science, Computational Linguistics and
Intelligent Text Processing, 7181: 311-322, 2012.
[4] Mohammed Attia, Antonio Toral, Lamia Tounsi, Monica Monachini and Josef van Genabith. 2010.
‘An automatically built Named Entity lexicon for Arabic’. LREC 2010. Valletta, Malta.
[5] J-P Ng and M-Y Kan, “QANUS: AN Open Source Question-Answering Platform”, 2010,
http://wing.comp.nus.edu.sg/~junping/docs/qanus.pdf
[6] Nico Schlaefer, “A Semantic Approach to Question Answering”.
VDM Verlag Dr. Mueller, ISBN 3836450739, 2007.
[7] Recommender Systems, http://www.cc.uah.es/drg/courses/datamining/IntroRecSys.pdf
[8] Dipanjan Das and André F.T. Martins, “A Survey on Automatic Text Summarization”,
Literature Survey for the Language and Statistics II course at Carnegie Mellon University, 2007
[9] Cunningham H, Maynard D, Bontcheva K, Tablan V, Aswani N, Roberts I et al. Text Processing with
GATE (Version 6). University of Sheffield Department of Computer Science, 2011.
[10] Oudah, M. and Shaalan, K. Person Name Recognition Using the Hybrid Approach. Lecture
Notes in Computer Science, Natural Language Processing and Information Systems, Springer Berlin
Heidelberg, vol. 7934, pages 237–248, 2013.
[11] Shaalan, K., Oudah, M., A Hybrid Approach to Arabic Named Entity Recognition, Journal of
Information Science (JIS), 40(1): 67-87, SAGE Publications Ltd, UK, 2014.
[12] Al-Ayyoub, Nuseir, Alsmearat, Jararweh, Gupta, M. Al-Ayyoub, A. Nuseir, K. Alsmearat, Y.
Jararweh, B. Gupta. Deep learning for arabic NLP: A survey, Journal of Computational Science, 26
(2018), pp. 522-531, 2018. Knowledge Representation and Reasoning Knowledge Representation and Reasoning Knowledge Representation and Reasoning Knowledge Representation and Reasoning Knowledge Representation and Reasoning Knowledge Representation and Reasoning Knowledge Representation and Reasoning Knowledge Representation and Reasoning Knowledge Representation and Reasoning Knowledge Representation and Reasoning Knowledge Representation and Reasoning Knowledge  Representation and Reasoning Knowledge Representation and Reasoning


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