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dc.contributor.advisorAgirre Bengoa, Eneko ORCIDes
dc.contributor.advisorRigau Claramunt, Germánes
dc.contributor.authorGonzález Agirre, Aitor
dc.description.abstract[EN]Measuring semantic similarity and relatedness between textual items (words, sentences, paragraphs or even documents) is a very important research area in Natural Language Processing (NLP). In fact, it has many practical applications in other NLP tasks. For instance, Word Sense Disambiguation, Textual Entailment, Paraphrase detection, Machine Translation, Summarization and other related tasks such as Information Retrieval or Question Answering. In this masther thesis we study di erent approaches to compute the semantic similarity between textual items. In the framework of the european PATHS project1, we also evaluate a knowledge-base method on a dataset of cultural item descriptions. Additionaly, we describe the work carried out for the Semantic Textual Similarity (STS) shared task of SemEval-2012. This work has involved supporting the creation of datasets for similarity tasks, as well as the organization of the task
dc.subjectsemantic textual similarityes
dc.subjecttextual similarityes
dc.subjectcultural heritagees
dc.titleExploring Semantic Textual Similarityes
dc.rights.holderAttribution-NonCommercial-ShareAlike 4.0 International*

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Attribution-NonCommercial-ShareAlike 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International