UNIBA: Super Sense Tagging at EVALITA 2011                                           Pierpaolo Basile                     ...
MotivationMotivation         Super Sense Tagging as sequence labelling problem [1]         Supervised approach            ...
WordSpaceWordSpace                You shall know a word                by the company it                keeps! [5]        ...
WordSpace   Random IndexingWordSpace: Random IndexingRandom Indexing [4]         builds WordSpace using document as contex...
WordSpace   Random IndexingWordSpace: Random IndexingFormally Random Indexing is based on Random Projection [2]           ...
WordSpace     Random IndexingWordSpace: context        Two WordSpaces using a        different definition of context        ...
MethodologyMethodology         Learning method: LIBLINEAR (SVM) [3]         Features            1    word, lemma, PoS-tag,...
EvaluationEvaluation                                          Table: Results of the evaluation                   System   ...
Final RemarksFinal Remarks         Main motivation: distributional features tackle data sparseness         problem in SST ...
Final Remarks                               That’s all folks!Pierpaolo Basile (basilepp@di.uniba.it)            SST Uniba ...
Final RemarksFor Further Reading I        Ciaramita, M., Altun, Y.: Broad-coverage sense disambiguation and        informa...
Final RemarksFor Further Reading II        Sahlgren, M.: An introduction to random indexing. In: Methods and        Applic...
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Sst evalita2011 basile_pierpaolo

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Sst evalita2011 basile_pierpaolo

  1. 1. UNIBA: Super Sense Tagging at EVALITA 2011 Pierpaolo Basile basilepp@di.uniba.it Department of Computer Science University of Bari “A. Moro” (ITALY) EVALITA 2011, Rome, 24-25 January 2012Pierpaolo Basile (basilepp@di.uniba.it) SST Uniba 24/01/2012 1 / 12
  2. 2. MotivationMotivation Super Sense Tagging as sequence labelling problem [1] Supervised approach lexical/linguistic features distributional features Main motivation: tackle the data sparseness problem using word similarity in a WordSpacePierpaolo Basile (basilepp@di.uniba.it) SST Uniba 24/01/2012 2 / 12
  3. 3. WordSpaceWordSpace You shall know a word by the company it keeps! [5] Words are represented as points in a geometric space Words are related if they are close in that spacePierpaolo Basile (basilepp@di.uniba.it) SST Uniba 24/01/2012 3 / 12
  4. 4. WordSpace Random IndexingWordSpace: Random IndexingRandom Indexing [4] builds WordSpace using document as context no matrix factorization required word-vectors are inferred using an incremental strategy 1 a random vector is assigned to each context sparse, high-dimensional and ternary ({-1, 0, 1}) a small number of randomly distributed non-zero elements 2 random vectors are accumulated incrementally by analyzing contexts in which terms occur word-vector assigned to each word is the sum of the random vectors of the contexts in which the term occurPierpaolo Basile (basilepp@di.uniba.it) SST Uniba 24/01/2012 4 / 12
  5. 5. WordSpace Random IndexingWordSpace: Random IndexingFormally Random Indexing is based on Random Projection [2] An,m · R m,k = B n,k k<m (1)where An,m is, for example, a term-doc matrixAfter projection the distance between points is preserved: d = c· drPierpaolo Basile (basilepp@di.uniba.it) SST Uniba 24/01/2012 5 / 12
  6. 6. WordSpace Random IndexingWordSpace: context Two WordSpaces using a different definition of context Wikipediap : a random Table: WordSpaces info vector is assigned to each Wikipedia page WordSpace C D Wikipediac : a random vector is assigned to Wikipediap 1,617,449 4,000 each Wikipedia Wikipediac 98,881 1,000 category categories can identify more general concepts C=number of contexts in the same way of D=vector dimension super-sensesPierpaolo Basile (basilepp@di.uniba.it) SST Uniba 24/01/2012 6 / 12
  7. 7. MethodologyMethodology Learning method: LIBLINEAR (SVM) [3] Features 1 word, lemma, PoS-tag, the first letter of the PoS-tag 2 the super-sense assigned to the most frequent sense of the word computed according to sense frequency in MultiSemCor 3 word starts with an upper-case character 4 grammatical conjugation (e.g. -are, -ere and -ire for Italian verbs) 5 distributional features: word-vector in the WordSpacePierpaolo Basile (basilepp@di.uniba.it) SST Uniba 24/01/2012 7 / 12
  8. 8. EvaluationEvaluation Table: Results of the evaluation System A P R F close 0.8696 0.7485 0.7583 0.7534 no distr feat 0.8822 0.7728 0.7818 0.7773 Wikipediac 0.8877 0.7719 0.8020 0.7866 Wikipediap 0.8864 0.7700 0.7998 0.7846Pierpaolo Basile (basilepp@di.uniba.it) SST Uniba 24/01/2012 8 / 12
  9. 9. Final RemarksFinal Remarks Main motivation: distributional features tackle data sparseness problem in SST task increment in recall proves our idea Further work: try a different supervised approach more suitable for sequence labelling task in this first attempt we are not interested in the learning method performance itselfPierpaolo Basile (basilepp@di.uniba.it) SST Uniba 24/01/2012 9 / 12
  10. 10. Final Remarks That’s all folks!Pierpaolo Basile (basilepp@di.uniba.it) SST Uniba 24/01/2012 10 / 12
  11. 11. Final RemarksFor Further Reading I Ciaramita, M., Altun, Y.: Broad-coverage sense disambiguation and information extraction with a supersense sequence tagger. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. pp. 594–602. Association for Computational Linguistics (2006) Dasgupta, S., Gupta, A.: An elementary proof of a theorem of Johnson and Lindenstrauss. Random Structures & Algorithms 22(1), 60–65 (2003) Fan, R., Chang, K., Hsieh, C., Wang, X., Lin, C.: Liblinear: A library for large linear classification. The Journal of Machine Learning Research 9, 1871–1874 (2008)Pierpaolo Basile (basilepp@di.uniba.it) SST Uniba 24/01/2012 11 / 12
  12. 12. Final RemarksFor Further Reading II Sahlgren, M.: An introduction to random indexing. In: Methods and Applications of Semantic Indexing Workshop at the 7th International Conference on Terminology and Knowledge Engineering, TKE. vol. 5 (2005) Sahlgren, M.: The Word-Space Model: Using distributional analysis to represent syntagmatic and paradigmatic relations between words in high-dimensional vector spaces. Ph.D. thesis, Stockholm: Stockholm University, Faculty of Humanities, Department of Linguistics (2006)Pierpaolo Basile (basilepp@di.uniba.it) SST Uniba 24/01/2012 12 / 12

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