1. Seminar Topics and Projects Giuseppe Attardi Dipartimento di Informatica Università di Pisa

2. Sentiment Analysis Data from Evalita 2014: Data from Evalita 2014:  Corpus of annotated tweets Experiment using Word Embeddings Experiment using Word Embeddings  Use DeepNL library: https://github.com/attardi/deepnl  Collect positive/negative tweets from Twitter, selecting those which contain positive/negative emoticons.  See also: http://districtdatalabs.silvrback.com/modern- methods-for-sentiment-analysis

3. Negation/Speculation Extraction Determine the scope of negative or speculative statements: Determine the scope of negative or speculative statements:  The lyso-platelet had no effect  MnlI-AluI could suppress the basal-level activity Approach: Approach:  Classifier for identifying cues  Classifier to determine scope Data Data  BioScope collection

4. Corpus of Product Reviews Download reviews from online shop Download reviews from online shop Classify as positive/negative according to stars Classify as positive/negative according to stars Train classifier to assign score Train classifier to assign score

5. Relation Extraction Exploit word embeddings as features + extra hand-coded features Exploit word embeddings as features + extra hand-coded features Use the Factor Based Compositional Embedding Model (FCM) http://www.cs.jhu.edu/~mrg/publications/finer e-naacl-2015.pdf Use the Factor Based Compositional Embedding Model (FCM) http://www.cs.jhu.edu/~mrg/publications/finer e-naacl-2015.pdf http://www.cs.jhu.edu/~mrg/publications/finer e-naacl-2015.pdf http://www.cs.jhu.edu/~mrg/publications/finer e-naacl-2015.pdf SemEval 2014 Relation Extraction data SemEval 2014 Relation Extraction data

6. Entity Linking with Embeddings Experiment with technique: Experiment with technique: R. Blanco, G. Ottaviano, E. Meiji. 2014. Fast and Space-Efficient Entity Linking in Queries. labs.yahoo.com/_c/uploads/WSDM-2015-blanco.pdf

7. Extraction of Semantic Hierarchies Use word embeddings as measure of semantic distance Use Wikipedia as source of text http://ir.hit.edu.cn/~jguo/papers/acl2014- hypernym.pdf http://ir.hit.edu.cn/~jguo/papers/acl2014- hypernym.pdf Aconitum Ranuncolacee Plant Organism

8. Suggested Topics for Seminars

9. Clustering Singular Value Decomposition Singular Value Decomposition S. Osiński, D. Weiss. 2004. Conceptual Clustering Using Lingo Algorithm: Evaluation on Open Directory Project Data. http://www.cs.put.poznan.pl/dweiss/site/publicatio ns/download/iipwm-osinski-weiss-stefanowski- 2004-lingo.pdf S. Osiński, D. Weiss. 2004. Conceptual Clustering Using Lingo Algorithm: Evaluation on Open Directory Project Data. http://www.cs.put.poznan.pl/dweiss/site/publicatio ns/download/iipwm-osinski-weiss-stefanowski- 2004-lingo.pdf http://www.cs.put.poznan.pl/dweiss/site/publicatio ns/download/iipwm-osinski-weiss-stefanowski- 2004-lingo.pdf http://www.cs.put.poznan.pl/dweiss/site/publicatio ns/download/iipwm-osinski-weiss-stefanowski- 2004-lingo.pdf S. Osiński, D. Weiss. 2005. A Concept-Driven Algorithm for Clustering Search Results. IEEE Intelligent Systems. http://dollar.biz.uiowa.edu/~nstreet/01439479.pdf S. Osiński, D. Weiss. 2005. A Concept-Driven Algorithm for Clustering Search Results. IEEE Intelligent Systems. http://dollar.biz.uiowa.edu/~nstreet/01439479.pdf

10. Recommender System Y. Koren. R. Bell. C. Volinski. Matrix Factorization Techniques for recommender systems. Y. Koren. R. Bell. C. Volinski. Matrix Factorization Techniques for recommender systems. http://www2.research.att.com/~volinsky/paper s/ieeecomputer.pdf

11. Opinion Mining B. Liu. Sentiment Analisis and Subjectivity. 2010. Handbook of NLP. http://www.cs.uic.edu/~liub/FBS/NLP- handbook-sentiment-analysis.pdf B. Liu. Sentiment Analisis and Subjectivity. 2010. Handbook of NLP. http://www.cs.uic.edu/~liub/FBS/NLP- handbook-sentiment-analysis.pdf http://www.cs.uic.edu/~liub/FBS/NLP- handbook-sentiment-analysis.pdf http://www.cs.uic.edu/~liub/FBS/NLP- handbook-sentiment-analysis.pdf

12. Semantic Role Labeling http://ufal.mff.cuni.cz/conll2009-st/task- description.html http://ufal.mff.cuni.cz/conll2009-st/task- description.html

13. Hierarchical Machine Translation A Hierarchical Phrase-Based Model for Statistical Machine Translation. David Chiang A Hierarchical Phrase-Based Model for Statistical Machine Translation. David Chiang www.isi.edu/~chiang/papers/chiang-acl05.pdf Translation by means of Word Embeddings Translation by means of Word Embeddings  J, Bengio 2014.

14. Recognizing Textual Entailment http://www.nist.gov/tac/2011/RTE/ http://www.nist.gov/tac/2011/RTE/

15. Question Answering Watson: Watson:  http://www.aaai.org/Magazine/Watson/watson.php http://www.aaai.org/Magazine/Watson/watson.php TAC: TAC:  http://www.nist.gov/tac/2008/qa/index.html