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

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:  Collect positive/negative tweets from Twitter, selecting those which contain positive/negative emoticons.  See also: methods-for-sentiment-analysis

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

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

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) e-naacl-2015.pdf Use the Factor Based Compositional Embedding Model (FCM) e-naacl-2015.pdf e-naacl-2015.pdf e-naacl-2015.pdf SemEval 2014 Relation Extraction data SemEval 2014 Relation Extraction data

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

Extraction of Semantic Hierarchies Use word embeddings as measure of semantic distance Use Wikipedia as source of text hypernym.pdf hypernym.pdf Aconitum Ranuncolacee Plant Organism

Suggested Topics for Seminars

Clustering Singular Value Decomposition Singular Value Decomposition S. Osiński, D. Weiss Conceptual Clustering Using Lingo Algorithm: Evaluation on Open Directory Project Data. ns/download/iipwm-osinski-weiss-stefanowski lingo.pdf S. Osiński, D. Weiss Conceptual Clustering Using Lingo Algorithm: Evaluation on Open Directory Project Data. ns/download/iipwm-osinski-weiss-stefanowski lingo.pdf ns/download/iipwm-osinski-weiss-stefanowski lingo.pdf ns/download/iipwm-osinski-weiss-stefanowski lingo.pdf S. Osiński, D. Weiss A Concept-Driven Algorithm for Clustering Search Results. IEEE Intelligent Systems. S. Osiński, D. Weiss A Concept-Driven Algorithm for Clustering Search Results. IEEE Intelligent Systems.

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. s/ieeecomputer.pdf

Opinion Mining B. Liu. Sentiment Analisis and Subjectivity Handbook of NLP. handbook-sentiment-analysis.pdf B. Liu. Sentiment Analisis and Subjectivity Handbook of NLP. handbook-sentiment-analysis.pdf handbook-sentiment-analysis.pdf handbook-sentiment-analysis.pdf

Semantic Role Labeling description.html description.html

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 Translation by means of Word Embeddings Translation by means of Word Embeddings  J, Bengio 2014.

Recognizing Textual Entailment

Question Answering Watson: Watson:  TAC: TAC: 