Sentiment Composition Using a Parabolic Model

International audienceIn this paper, we propose a computational model that accounts for the effects of negation and modality on opinion expressions. Based on linguistic experiments informed by native speakers, we distil these effects according to the type of modality and negation. The model relies on a parabolic representation where an opinion expression is represented as a point on a parabola. Negation is modelled as functions over this parabola whereas modality through a family of parabolas of different slopes; each slope corresponds to a different certainty degree. The model is evaluated using two experiments, one involving direct strength judgements on a 7-point scale and the other relying on a sentiment annotated corpus. The empirical evaluation of our model shows that it matches the way humans handle negation and modality in opinionated sentence

Generating cooperative question-responses by means of erotetic search scenarios

The concept of cooperative question-responses as an extension of cooperative behaviours used by interfaces for databases and information systems is proposed. A procedure to generate question-responses based on question dependency and erotetic search scenarios is presented. The procedure is implemented in Prolog

On the Learnability of Programming Language Semantics

This is the final version of the article. Available from ICE via the DOI in this record.Game semantics is a powerful method of semantic analysis for programming languages. It gives mathematically accurate models ("fully abstract") for a wide variety of programming languages. Game semantic models are combinatorial characterisations of all possible interactions between a term and its syntactic context. Because such interactions can be concretely represented as sets of sequences, it is possible to ask whether they can be learned from examples. Concretely, we are using long short-term memory neural nets (LSTM), a technique which proved effective in learning natural languages for automatic translation and text synthesis, to learn game-semantic models of sequential and concurrent versions of Idealised Algol (IA), which are algorithmically complex yet can be concisely described. We will measure how accurate the learned models are as a function of the degree of the term and the number of free variables involved. Finally, we will show how to use the learned model to perform latent semantic analysis between concurrent and sequential Idealised Algol

On the Learnability of Programming Language Semantics

Game semantics is a powerful method of semantic analysis for programming languages. It gives mathematically accurate models ("fully abstract") for a wide variety of programming languages. Game semantic models are combinatorial characterisations of all possible interactions between a term and its syntactic context. Because such interactions can be concretely represented as sets of sequences, it is possible to ask whether they can be learned from examples. Concretely, we are using long short-term memory neural nets (LSTM), a technique which proved effective in learning natural languages for automatic translation and text synthesis, to learn game-semantic models of sequential and concurrent versions of Idealised Algol (IA), which are algorithmically complex yet can be concisely described. We will measure how accurate the learned models are as a function of the degree of the term and the number of free variables involved. Finally, we will show how to use the learned model to perform latent semantic analysis between concurrent and sequential Idealised Algol.Comment: In Proceedings ICE 2017, arXiv:1711.1070

Question dependency in terms of compliance and erotetic implication

The dependency relation between questions is discussed in terms of compliance (developed within inquisitive semantics – INQ) and erotetic implication (developed within Inferential Erotetic Logic – IEL). I show that INQ approach to questions' dependency is more narrow and strict than the one offered by IEL

IEL-based Formal Dialogue System for Tutorials

FFormal dialogue system for tutorials DL(IEL)T is introduced. The system allows for modelling certain behaviours related to questioning agendas observed in tutorial dialogues. Inferential Erotetic Logic is the underlying logic of questions used here. Tutorial dialogues retrieved from the Basic Electricity and Electronics Corpus are presented and analysed with the use of DL(IEL)T