Natural Language Generation and Fuzzy Sets : An Exploratory Study on Geographical Referring Expression Generation

This work was supported by the Spanish Ministry for Economy and Competitiveness (grant TIN2014-56633-C3-1-R) and by the European Regional Development Fund (ERDF/FEDER) and the Galician Ministry of Education (grants GRC2014/030 and CN2012/151). Alejandro Ramos-Soto is supported by the Spanish Ministry for Economy and Competitiveness (FPI Fellowship Program) under grant BES-2012-051878.Postprin

Some views on information fusion and logic based approaches in decision making under uncertainty

Decision making under uncertainty is a key issue in information fusion and logic based reasoning approaches. The aim of this paper is to show noteworthy theoretical and applicational issues in the area of decision making under uncertainty that have been already done and raise new open research related to these topics pointing out promising and challenging research gaps that should be addressed in the coming future in order to improve the resolution of decision making problems under uncertainty

Cross-Linguistic Variation in the Meaning of Quantifiers: Implications for Pragmatic Enrichment

One of the most studied scales in the literature on scalar implicatures is the quantifier scale. While the truth of some is entailed by the truth of all, some is felicitous only when all is false. This opens the possibility that some would be felicitous if, e.g., almost all of the objects in the restriction of the quantifier have the property ascribed by the nuclear scope. This prediction from the standard theory of quantifier interpretation clashes with native speakers’ intuitions. In Experiment 1 we report a questionnaire study on the perception of quantifier meanings in English, French, Slovenian, and German which points to a cross-linguistic variation with respect to the perception of numerical bounds of the existential quantifier. In Experiment 2, using a picture choice task, we further examine whether the numerical bound differences correlate with differences in pragmatic interpretations of the quantifier some in English and quelques in French and interpret the results as supporting our hypothesis that some and its cross-linguistic counterparts are subjected to different processes of pragmatic enrichment

Ideal Words: A Vector-Based Formalisation of Semantic Competence

Funder: Università degli Studi di TrentoAbstractIn this theoretical paper, we consider the notion of semantic competence and its relation to general language understanding—one of the most sough-after goals of Artificial Intelligence. We come back to three main accounts of competence involving (a) lexical knowledge; (b) truth-theoretic reference; and (c) causal chains in language use. We argue that all three are needed to reach a notion of meaning in artificial agents and suggest that they can be combined in a single formalisation, where competence develops from exposure to observable performance data. We introduce a theoretical framework which translates set theory into vector-space semantics by applying distributional techniques to a corpus of utterances associated with truth values. The resulting meaning space naturally satisfies the requirements of a causal theory of competence, but it can also be regarded as some ‘ideal’ model of the world, allowing for extensions and standard lexical relations to be retrieved.</jats:p

Functional Distributional Semantics

Vector space models have become popular in distributional semantics, despite the challenges they face in capturing various semantic phenomena. We propose a novel probabilistic framework which draws on both formal semantics and recent advances in machine learning. In particular, we separate predicates from the entities they refer to, allowing us to perform Bayesian inference based on logical forms. We describe an implementation of this framework using a combination of Restricted Boltzmann Machines and feedforward neural networks. Finally, we demonstrate the feasibility of this approach by training it on a parsed corpus and evaluating it on established similarity datasets

A Neuro Symbolic Approach for Contradiction Detection in Persian Text

Detection of semantic contradictory sentences is a challenging and fundamental issue for some NLP applications, such as textual entailments recognition. In this study, contradiction means different types of semantic confrontation, such as negation, antonymy, and numerical. Due to the lack of sufficient data to apply precise machine learning and, specifically, deep learning methods to Persian and other low-resource languages, rule-based approaches are of great interest. Also, recently, the emergence of new methods such as transfer learning has opened up the possibility of deep learning for low-resource languages. This paper introduces a hybrid contradiction detection approach for detecting seven categories of contradictions in Persian texts: Antonymy, negation, numerical, factive, structural, lexical and world knowledge. The proposed method consists of 1) a novel data mining method and 2) a transformer-based deep neural method for contradiction detection . Also, a simple baseline is presented for comparison. The data mining method uses frequent rule mining to extract appropriate contradiction detection rules employing a development set. Extracted rules are tested for different categories of contradictory sentences. In the first step, a classifier checks whether the rules work for an input sentence pair. Then, according to the result, rules are used for three categories of negation, numerical, and antonym. In this part, the highest F-measure is obtained for detecting the negation category (90%), the average F-measure for these three categories is 86%, and for the other four categories, in which the rules have a lower F-measure of 62%, the transformer-based method achieved 76%. The proposed hybrid approach has an overall f-measure of higher than 80%.&amp;nbsp

The semantics of gradation

The term 'gradation' is meant to cover a range of phenomena which for the time being I shall call quantitative evaluations regarding dimensions or features. I shall actually be looking into the principles governing the way gradation is expressed in language. The quantitative aspect of the adjectives of dimension occupies a key position which can be systematically explained and this aspect will be the crucial point of the discussion. I shall focus on the various grammatical forms of comparison: comparative, equative, superlative and some related constructions, and indications of measurement and adverbial indications of degree