Unity in the Variety of Quotation

This chapter argues that while quotation marks are polysemous, the thread that runs through all uses of quotation marks that involve reference to expressions is pure quotation, in which an expression formed by enclosing another expression in quotation marks refers to that enclosed expression. We defend a version of the so-called disquotational theory of pure quotation and show how this device is used in direct discourse and attitude attributions, in exposition in scholarly contexts, and in so-called mixed quotation in indirect discourse and attitude attributions. We argue that uses of quotation marks that extend beyond pure quotation have two features in common. First, the expressions appearing in quotation marks are intended to be understood, and that they are intended to be understood is essential to the function that such quotations play in communication, though this does not always involve the expressions contributing their extensional properties to fixing truth conditions for the sentences in which they appear. Second, they appeal to a relation to the expression appearing in quotation marks that plays a role in determining the truth conditions of the sentences in which they appear

Presupposition, perceptional relativity and translation theory

The intertwining of assertions and presuppositions in utterances affects the way a text is perceived in the source language (SL) and the target language (TL). Presuppositions can be thought of as shared assumptions that form the background of the asserted meaning. To translate presuppositions as assertions, or vice versa, can distort the thematic meaning of the SL text and produce a text with a different information structure. Since a good translation is not simply concerned with transferring the propositional content of the SL text, but also its other semantic and pragmatic components, including thematic meaning, a special attention should be accorded to the translation of presupposition. This article examines the intrinsic relation between presupposition and thematic meaning, why the concept is relevant to translation theory, and how presupposition can affect the structure and understanding of discourse. Unshared presuppositions are major obstacles in translation, as cultural concepts may be conveyed through expressions that yield presuppositions. To attain an optimal proximity to the SL text, presupposition needs to be singled out as a distinct aspect of meaning, and distinctions need to be made between definite and indefinite meaning, topic and comment, topic and focus, presupposition and entailment, and presupposition and implicature

TURTLE-P: a UML profile for the formal validation of critical and distributed systems

The timed UML and RT-LOTOS environment, or TURTLE for short, extends UML class and activity diagrams with composition and temporal operators. TURTLE is a real-time UML profile with a formal semantics expressed in RT-LOTOS. Further, it is supported by a formal validation toolkit. This paper introduces TURTLE-P, an extended profile no longer restricted to the abstract modeling of distributed systems. Indeed, TURTLE-P addresses the concrete descriptions of communication architectures, including quality of service parameters (delay, jitter, etc.). This new profile enables co-design of hardware and software components with extended UML component and deployment diagrams. Properties of these diagrams can be evaluated and/or validated thanks to the formal semantics given in RT-LOTOS. The application of TURTLE-P is illustrated with a telecommunication satellite system

Probabilistic Operational Semantics for the Lambda Calculus

Probabilistic operational semantics for a nondeterministic extension of pure lambda calculus is studied. In this semantics, a term evaluates to a (finite or infinite) distribution of values. Small-step and big-step semantics are both inductively and coinductively defined. Moreover, small-step and big-step semantics are shown to produce identical outcomes, both in call-by- value and in call-by-name. Plotkin's CPS translation is extended to accommodate the choice operator and shown correct with respect to the operational semantics. Finally, the expressive power of the obtained system is studied: the calculus is shown to be sound and complete with respect to computable probability distributions.Comment: 35 page

Feature-based and Model-based Semantics for English, French and German Verb Phrases

This paper considers the relative merits of using features and formal event models to characterise the semantics of English, French and German verb phrases, and con- siders the application of such semantics in machine translation. The feature-based ap- proach represents the semantics in terms of feature systems, which have been widely used in computational linguistics for representing complex syntactic structures. The paper shows how a simple intuitive semantics of verb phrases may be encoded as a feature system, and how this can be used to support modular construction of au- tomatic translation systems through feature look-up tables. This is illustrated by automated translation of English into either French or German. The paper contin- ues to formalise the feature-based approach via a model-based, Montague semantics, which extends previous work on the semantics of English verb phrases. In so doing, repercussions of and to this framework in conducting a contrastive semantic study are considered. The model-based approach also promises to provide support for a more sophisticated approach to translation through logical proof; the paper indicates further work required for the fulfilment of this promise

Using a Probabilistic Class-Based Lexicon for Lexical Ambiguity Resolution

This paper presents the use of probabilistic class-based lexica for disambiguation in target-word selection. Our method employs minimal but precise contextual information for disambiguation. That is, only information provided by the target-verb, enriched by the condensed information of a probabilistic class-based lexicon, is used. Induction of classes and fine-tuning to verbal arguments is done in an unsupervised manner by EM-based clustering techniques. The method shows promising results in an evaluation on real-world translations.Comment: 7 pages, uses colacl.st

Cross-Lingual Classification of Crisis Data

Many citizens nowadays flock to social media during crises to share or acquire the latest information about the event. Due to the sheer volume of data typically circulated during such events, it is necessary to be able to efficiently filter out irrelevant posts, thus focusing attention on the posts that are truly relevant to the crisis. Current methods for classifying the relevance of posts to a crisis or set of crises typically struggle to deal with posts in different languages, and it is not viable during rapidly evolving crisis situations to train new models for each language. In this paper we test statistical and semantic classification approaches on cross-lingual datasets from 30 crisis events, consisting of posts written mainly in English, Spanish, and Italian. We experiment with scenarios where the model is trained on one language and tested on another, and where the data is translated to a single language. We show that the addition of semantic features extracted from external knowledge bases improve accuracy over a purely statistical model