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 one the subtracks (i.e. raw input data, Student Answers), among 12 runs submitted, and the approach proved to be very robust across the different datasets.
We present Contrast-Ita Bank, a corpus annotated with discourse contrast relations in Italian. We annotate both explicit and implicit contrast relations, following the schema proposed in the Penn Discourse Treebank. We provide and discuss quantitative data about the new resource.
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 listing the tags we decided to use, we report the results of the experiment we performed to test the annotation scheme, in terms of interannotation agreement and linguistic analysis of annotated data.
A verb argument position can be described by the semantic type that characterizes the words filling that position. We investigate a number of linguistic issues underlying the tagging of an Italian corpus with the semantic types provided by the T-PAS (Typed Predicate-Argument Structure) resource. Our main interest is to evaluate whether our annotation methodology can be employed effectively for the extension of the annotation of the corpus associated with the resource. In order to achieve this goal we compare quantitative data about the tagging and qualitative data derived from the Inter-Annotator Agreement.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.