Anna Feltracco

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We present the system developed at FBK for the SemEval 2016 Shared Task 2 ”Interpretable Semantic Textual Similarity” as well as the results of the submitted runs. We use a single neural network classification model for predicting the alignment at chunk level, the relation type of the alignment and the similarity scores. Our best run was ranked as first in(More)
The goal of this paper is to introduce T-PAS, a resource of typed predicate argument structures for Italian, acquired from corpora by manual clustering of distributional information about Italian verbs, to be used for linguistic analysis and semantic processing tasks. T-PAS is the first resource for Italian in which semantic selection properties and(More)
English. We present the system developed at FBK for the EVALITA 2016 Shared Task “QA4FAQ – Question Answering for Frequently Asked Questions”. A peculiar characteristic of this task is the total absence of training data, so we created a meaningful representation of the data using only word embeddings. We present the system as well as the results of the two(More)
In this paper we propose a scheme for annotating opposition relations among verb frames in lexical resources. The scheme is tested on the T-PAS resource, an inventory of typed predicate argument structures for Italian, conceived for both linguistic research and computational tasks. After discussing opposition relations from a linguistic point of view and(More)
English. This paper presents the first release of LICO, a Lexicon for Italian COnnectives. LICO includes about 170 discourse connectives used in Italian, together with their orthographical variants, part of speech(es), semantic relation(s) (according to the Penn Discourse Treebank relation catalogue), and a number of usage examples. Italiano. Questo(More)
We describe an experiment for the acquisition of opposition relations among Italian verb senses, based on a crowdsourcing methodology. The goal of the experiment is to discuss whether the types of opposition we distinguish (i.e. complementarity, antonymy, converseness and reversiveness) are actually perceived by the crowd. In particular, we collect data for(More)
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