seminar topics and projects giuseppe attardi dipartimento di informatica università di pisa

Seminar Topics and Projects

Giuseppe AttardiGiuseppe AttardiDipartimento di InformaticaDipartimento di Informatica

Università di PisaUniversità di Pisa

Università di Pisa

Sentiment AnalysisSentiment Analysis

Data from Evalita 2014:Data from Evalita 2014: Corpus of annotated tweets

Experiment using Word EmbeddingsExperiment 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

Negation/Speculation Negation/Speculation ExtractionExtraction Determine the scope of negative or Determine the scope of negative or

speculative statements: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

DataData BioScope collection

Corpus of Product ReviewsCorpus of Product Reviews

Download reviews from online shopDownload reviews from online shop Classify as positive/negative according to Classify as positive/negative according to

starsstars Train classifier to assign scoreTrain classifier to assign score

Relation ExtractionRelation Extraction

Exploit word embeddings as features + extra Exploit word embeddings as features + extra hand-coded featureshand-coded features

Use the Factor Based Compositional Use the Factor Based Compositional Embedding Model (FCM)Embedding Model (FCM)http://www.cs.jhu.edu/~mrg/publications/finere-naacl-2015.pdf

SemEval 2014 Relation Extraction dataSemEval 2014 Relation Extraction data

Entity Linking with Entity Linking with EmbeddingsEmbeddings 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

Extraction of Semantic Extraction of Semantic HierarchiesHierarchies Use word embeddings as measure of

semantic distance Use Wikipedia as source of text http://ir.hit.edu.cn/~jguo/papers/acl2014-

hypernym.pdf

Aconitum

Ranuncolacee

Plant

Organism

Suggested Topics for Suggested Topics for SeminarsSeminars

ClusteringClustering

Singular Value DecompositionSingular Value Decomposition S. Osiński, D. Weiss. 2004.S. Osiński, D. Weiss. 2004. Conceptual Conceptual

Clustering Using Lingo Algorithm: Evaluation on Clustering Using Lingo Algorithm: Evaluation on Open Directory Project Data. Open Directory Project Data. http://www.cs.put.poznan.pl/dweiss/site/publicatihttp://www.cs.put.poznan.pl/dweiss/site/publications/download/iipwm-osinski-weiss-stefanowski-ons/download/iipwm-osinski-weiss-stefanowski-2004-lingo.pdf2004-lingo.pdf

S. Osiński, D. Weiss. 2005. A Concept-Driven S. Osiński, D. Weiss. 2005. A Concept-Driven Algorithm for Clustering Search Results. IEEE Algorithm for Clustering Search Results. IEEE Intelligent Systems. Intelligent Systems. http://dollar.biz.uiowa.edu/~nstreet/01439479.pdfhttp://dollar.biz.uiowa.edu/~nstreet/01439479.pdf

Recommender SystemRecommender System

Y. Koren. R. Bell. C. Volinski. Matrix Y. Koren. R. Bell. C. Volinski. Matrix Factorization Techniques for recommender Factorization Techniques for recommender systems.systems.

http://www2.research.att.com/~volinsky/http://www2.research.att.com/~volinsky/papers/ieeecomputer.pdfpapers/ieeecomputer.pdf

Opinion MiningOpinion Mining

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

Semantic Role LabelingSemantic Role Labeling

http://ufal.mff.cuni.cz/conll2009-st/task-http://ufal.mff.cuni.cz/conll2009-st/task-description.htmldescription.html

Hierarchical Machine Hierarchical Machine TranslationTranslation A A HierarchicalHierarchical Phrase-Based Model for Phrase-Based Model for

Statistical Statistical Machine TranslationMachine Translation. David Chiang. David Chiang

www.isi.edu/~chiang/papers/chiang-acl05.pdfwww.isi.edu/~chiang/papers/chiang-acl05.pdf Translation by means of Word EmbeddingsTranslation by means of Word Embeddings

J, Bengio 2014.

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

Question AnsweringQuestion Answering

Watson:Watson: http://www.aaai.org/Magazine/Watson/watson.php

TAC:TAC: http://www.nist.gov/tac/2008/qa/index.html