syllabus
1. Information regarding the programme
1.1 Higher education institution 	Babeş Bolyai University	
1.2 Faculty	Faculty of Mathematics and Computer Science	
1.3 Department	Department of Computer Science	
1.4 Field of study	Computer Science	
1.5 Study cycle	Master	
1.6 Study programme / Qualification 	Computer Science	

2. Information regarding the discipline 
2.1 Name of the discipline	Computational Approaches for  Natural Language Semantics	
2.2 Course coordinator 	Lect.dr. Lupea Mihaiela	
2.3 Seminar coordinator	Lect.dr. Lupea Mihaiela	
2.4. Year of study	2	2.5 Semester	2	2.6. Type of evaluation	exam	2.7 Type of discipline	compulsory	

3. Total estimated time (hours/semester of didactic activities) 
3.1 Hours per week 	3	Of which: 3.2 course	2	3.3 seminar/laboratory	1	
3.4 Total hours in the curriculum 	36	Of which: 3.5 course	24	3.6 seminar/laboratory	12	
Time allotment:	hours	
Learning using manual, course support, bibliography, course notes	40	
Additional documentation (in libraries, on electronic platforms, field documentation) 	14	
Preparation for seminars/labs, homework, papers, portfolios and essays	34	
Tutorship	14	
Evaluations	30	
Other activities: individual project	32	
3.7 Total individual study hours 	164	
3.8 Total hours per semester	200	
3.9 Number of ECTS credits	8	

4. Prerequisites (if necessary)
4.1. curriculum	•	the course Knowledge Based Systems and Language Technology	
4.2. competencies	•		

5. Conditions (if necessary)
5.1. for the course	•		
5.2.  for the seminar /lab activities	•	Laboratory with computers; high level programming language environment (.NET or any Java environement a.s.o.)	

6. Specific competencies acquired 
Professional competencies	•	Knowledge, understanding and use of theoretical concepts, tasks, applications  at the semantic level of Natural Language Processing. 
•	Apply and use statistic models, artificial intelligence algorithms (clustering, machine learning) and techniques (unsupervised, supervised) to solve different tasks at the semantic level (textual entailment, information retrieval, document summarization, text categorization and segmentation, machine transltion) in NLP domain.
	
Transversal competencies	•	Handling human language by computer needs a semantic approach of the natural language processing.  
•	The web space is a semantic one and needs semantic applications as: text categorization, text clustering, concept/entity extraction, sentiment/opinion analysis, document summarization, and entity relation modeling. 	

7. Objectives of the discipline (outcome of the acquired competencies)
7.1 General objective of the discipline
	•	To provide the  theoretical concepts and techniques for a computational approach of Natural Language Semantics. 
•	To understand the current state of the art in Natural Language Processing  in order to realize original research in NLP.	
7.2 Specific objective of the discipline
	•	To apply and use statistic models, artificial intelligence algorithms and techniques to solve different tasks at the and semantic level (information retrieval, textual entailment, document summarization,text segmentation, text clustering, machine translation)  in NLP domain.

8. Content
8.1 Course	Teaching methods	Remarks	
1.	Textual entailment (TE) as a step of text understanding. Validation of TE . RTE contests.	Exposure: description, explanation, examples, debate, dialogue		
2.	Textual entailment (TE) 
-	methods and approaches	Exposure: description, explanation, examples, debate, dialogue		
3.	Information extraction- Named Entity Recognition (NER)	Exposure: description, explanation, examples, debate, dialogue		
4.	Document summarization	Exposure: description, explanation, examples, debate, dialogue		
5.	Multi-document summarization	Exposure: description, explanation, examples, debate, dialogue		
6.	Information retrieval I	Exposure: description, explanation, examples, debate, dialogue		
7.	Information retrieval II	Exposure: description, explanation, examples, debate, dialogue		
8.	Students’ presentations of the theoretical paper	Debate, dialogue		
9.	Text categorization, text clustering and text segmentation	Exposure: description, explanation, examples, debate, dialogue		
10.	Machine translation II	Exposure: description, explanation, examples, debate, dialogue		
11.	Machine translation II	Exposure: description, explanation, examples, debate, dialogue		
12.	Students’ presentations of the practical project	Debate, dialogue		

Bibliography
J.ALLEN : Natural language understanding, Benjamin/Cummings Publ. , 2nd ed., 1995. 
2. E. CHARNIAK: "Statistical language learning", MIT press, 1996.  
3. B.CARPENTER: ALE: The attribute logic engine. User's guide. Carnegie Mellon University,1994.
4. H. Helbig: "Knowledge Representation and the Semantics of Natural Language", Springer, 2006. 
5. D.JURAFSKY, J.MARTIN: Speech and language processing, Prentice Hall, 2000.
6. C.MANNING, H.SCHUTZE: Foundation of statistical natural language processing, MIT, 1999.
7. (Editor) R. MITKOV: The Oxford Handbook of Computational Linguistics, Oxford University Press, 2003.
8. S.J.RUSSELL, P.NORVIG: Artificial intelligence. A modern approach, Prentice-Hall International,1995.
9. D.TATAR: Inteligenta artificiala: demonstrare automata de teoreme, prelucrarea limbajului natural, Editura Albastra, Microinformatica, 2001.
10. D. TATAR: Inteligenta artificiala. Aplicatii in prelucrarea limbajului natural, Editura Albastra, Microinformatica, 2003, ISBN 973-650-100-0	
8.2 Seminar / laboratory	Teaching methods	Remarks	
1.	Word similarity measures in WordNet	Explanation, dialogue, case studies	The seminar is structured as 2 hours classes every second week	
2.	Students’ presentations of a NLP domain and a corresponding tool	Explanation, dialogue, case studies		
3.	Working with dedicated tools for NER	Explanation, dialogue, case studies		
4.	Boolean model and vector space model in Information retrieval – a manual example 	Explanation, dialogue, case studies		
5.	Working with tools for machine translation	Explanation, dialogue, case studies		
6.	Students’ presentations of the practical project.	Explanation, dialogue, case studies		
Bibliography
	
1. Rada Mihalcea: www.cs.unt.edu/~rada/downloads.html
2. Resurse lingvistice in limba romana: www.racai.ro	

9. Corroborating the content of the discipline with the expectations of the epistemic community, professional associations and representative employers within the field of the program

•	The course respects the IEEE and ACM Curricula Recommendations for Computer Science studies;
•	The course exists in the studying program as a graduate course in some major universities in Romania and  abroad;
•	In the semantic web era, a growing number of semantic applications started to access and interoperate through the internet. Text mining, includes text categorization, text clustering, concept/entity extraction, sentiment analysis, document summarization, and entity relation modeling.	

10. Evaluation
Type of activity	10.1 Evaluation criteria	10.2 Evaluation methods	10.3 Share in the grade (%)	
10.4 Course	- know the theoretical concepts of the domain;
- apply the course methods, algorithms in problem solving	Written exam	40%	
10.5 Seminar/lab activities	-  know to write an overview of a specific domain 	Presentation of a NLP domain and a corresponding tool;
	15%	
	-know to synthesize and compare different approaches/results of the same studied subject.	Theoretical paper based on  recent research papers in NLP domain;	20%	
	-be able to implement course algorithms
	Practical project -implementation of a NLP tool based on the studied methods	25%	
10.6 Minimum performance standards	
•	At least grade 5 (from a scale of 1 to 10) at all four evaluation stages.	

Date				Signature of course coordinator 	Signature of seminar coordinator
..........................		Lect.dr. Lupea Mihaiela		 Lect.dr. Lupea Mihaiela

Date of approval  					  Signature of the head of department	
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