SSN_NLP@SardiStance : Stance Detection from Italian Tweets using RNN and Transformers

Stance detection refers to the detection of one’s opinion about the target from their statements. The aim of sardistance task is to classify the Italian tweets into classes of favor, against or no feeling towards the target. The task has two sub-tasks : in Task A, the classification has to be done by considering only the textual meaning whereas in Task B the tweets must be classified by considering the contextual information along with the textual meaning. We have presented our solution to detect the stance utilizing only the textual meaning (Task A) using encoder-decoder model and transformers. Among these two approaches, simple transformers have performed better than the encoder-decoder model with an average F1-score of 0.4707

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

DisLoP: Towards a Disjunctive Logic Programming System

This paper gives a brief high-level description of the implementation of a disjunctive logic programming system referred to as DisLoP. This system is a result of research activities of the Disjunctive Logic Programming-project (funded by Deutsche Forschungs-Gemeinschaft), undertaken by the University of Koblenz since July 1995

A Rational and Efficient Algorithm for View Deletion in Databases

In this paper, we show how techniques from disjunctive logic programming and classical first-order theorem proving can be used for efficient (deductive) database updates. The key idea is to tranform the given database together with the update request into a disjunctive logic program and apply disjunctive techniques (such as minimal model reasoning) to solve the original update problem. We present two variants of our algorithm both of which are of polynomial space complexity. One variant, which is based on offline preprocessing, is of polynomial time complexity. We also show that both variants are rational in the sense that they satisfy certain rationality postulates stemming from philosophical works on belief dynamics

On The Correctness Of Unfold/fold Transformation Of Normal And Extended Logic Programs

AbstractWe show that the framework for unfold/fold transformation of logic programs, first proposed by Tamaki and Sato and later extended by various researchers, preserves various nonmonotonic semantics of normal logic programs, especially preferred extension, partial stable models, regular model, and stable theory semantics. The primary aim of this research is to adopt a uniform approach for every semantics of normal programs, and that is elegantly achieved through the notion of semantic kernel. Later, we show that this framework can also be applied to extended logic programs, preserving the answer set semantics