A comparative analysis of the Slovenian particles še and že against the background of the Russian language

The article compares the Slovenian particles še and že mainly with the Russian particles еще and уже. Unlike the Russian particle уже, the Slovenian particle že cannot be combined with the negative form of the verb: instead of it, the adverb več is used in such contexts. The most subtle differences between the Slovenian and Russian languages are found in the combinations of še and že with temporal modifiers, where the so-called “plot time” is characteristic of the Slovenian language. The event is interpreted as localized on the time axis, not from the perspective of the «speaker’s time», which is manifested in the possibility of using že in such contexts as umrl je že v devetnajstem stoletju (he died in the nineteenth century already). Moreover, že is not able to express the meaning ‘no earlier / no later than’, because the particle šele replaces it in this function. The author comes to the conclusion that Slovenian particles are more widely used as modal than their Russian equivalents. The particle še has numerous intensifying functions, correlating with the functions of Russian particles даже, еще и, и. The particle že can be used in the same way as two Russian words уже and уж. In contrast to уж, že does not always express displeasure and can add the meaning of a concession to the statement.

Modularity and compositionality: The case of temporal modifiers

This paper concerns the possibility of building Montague grammars in a modular and incremental way. As an illustration, we investigate the following question. Given an atemporal Montague grammar, on the one hand, and a temporal ontology, on the other hand, is it possible to combine them into a new grammar such that it would: (i) conservatively extend the original grammar, and (ii) allow temporal modifiers to be accommodated.

Temporal modifiers and the Rogers-Aliant dispute

In this article, we discuss a recent dispute between two Canadian companies, Rogers and Aliant, which went before the Canadian Radio-television and Telecommunications Commission. This involved an apparently ambiguous provision in an agreement between these companies, the interpretation of which was widely seen to hinge on the placement of a single comma in this provision, although the dispute was ultimately resolved by reference to the unambiguous French version of this provision. We provide a syntactic and semantic analysis of the linguistic facts of the dispute, rejecting Aliant’s argument (and the CRTC’s original conclusion) that the placement of the comma provided robust evidence of the intended meaning of the disputed provision and showing how two temporal expressions in this provision, thereafter and prior, contribute to the meaning advanced by Rogers. We also demonstrate the essential equivalence of this meaning to that of the French version of the agreement.

Learning Methods to Combine Linguistic Indicators: Improving Aspectual Classification and Revealing Linguistic Insights

Aspectual classification maps verbs to a small set of primitive categories in order to reason about time. This classification is necessary for interpreting temporal modifiers and assessing temporal relationships, and is therefore a required component for many natural language applications. A verb's aspectual category can be predicted by co-occurrence frequencies between the verb and certain linguistic modifiers. These frequency measures, called linguistic indicators, are chosen by linguistic insights. However, linguistic indicators used in isolation are predictively incomplete, and are therefore insufficient when used individually. In this article, we compare three supervised machine learning methods for combining multiple linguistic indicators for aspectual classification: decision trees, genetic programming, and logistic regression. A set of 14 indicators are combined for classification according to two aspectual distinctions. This approach improves the classification performance for both distinctions, as evaluated over unrestricted sets of verbs occurring across two corpora. This demonstrates the effectiveness of the linguistic indicators and provides a much-needed full-scale method for automatic aspectual classification. Moreover, the models resulting from learning reveal several linguistic insights that are relevant to aspectual classification. We also compare supervised learning methods with an unsupervised method for this task.

Russisch byt' ('sein') als funktionale und/oder lexikalische Kategorie

The present study offers an analysis of the Russian copular constructions with predicate nominals. In such copular sentences two cases may mark the predicate: the nominative and the instrumental as in 'Anna byla medsestra/medsestroj' - 'Anna was-3sg.fem.a nursenom/instr'. In the present tense the copula has a null-form and the predicate nominal can only be in the nominative. I argue that the case alternation corresponds to the distinction of Stage Level and Individual Level Predicates in the sense of Kratzer (1994) and Diesing (1992), but with some objections. The copula with Instrumental forms S-Predicates, which are analyzed as predicates applying to situations referring to time. The copula with nominative forms I-Predicates, which attribute properties to individuals without referring to time. I-Predicates have no situation argument. Data that show the (in-)compatibility of copular sentences with certain spatial or temporal modifiers provide a reason to assume a situation argument in byt' + Instr but not in byt' + Nom. Byt' behaves differently in different grammatical contexts: in contexts of sentence negation, yes/no-questions and under focus byt' + Instr behaves like a lexical category, while byt' + Nom behaves like a functional category. As a functional category byt' + Nom is non-overt in the present and is always finite. The semantic distinction between nominative and instrumental predicate NPs is pegged to an opposition between a structure with a functional copula as the only tense and agreement marker with base position in TP and a lexical copula in VP (Franks 1995, Bailyn&Rubin 1991). To explain phenomena of the copula in Russian I propose an integrated syntactic model for two copulas. The two copulas may be conceived as distinct realizations of one verbal lexical entry which will be specified as a lexical or as functional category in the course of lexical insertion. The Model of Parallel Morphology might be used to explain this phenomenon.

Interpretation of Some Terms and Phrases in Public Weather Forecasts

This paper reports some results of a study in which two groups of individuals—undergraduate students and professional meteorologists at Oregon State University—completed a short questionnaire concerning their interpretations of terminology commonly used in public weather forecasts. The questions related to terms and phrases associated with three elements: 1) cloudiness—fraction of sky cover; 2) precipitation—spatial and/or temporal variations; and 3) temperature—specification of intervals. The students' responses indicate that cloudiness terms are subject to wide and overlapping ranges of interpretation, although the interpretations of these terms correspond quite well to National Weather Service definitions. Their responses to the precipitation and temperature questions reveal that some confusion exists concerning the meaning of spatial and temporal modifiers in precipitation forecasts and that some individuals interpret temperature ranges in terms of asymmetric intervals. When compared to the students' responses, the meteorologists' responses exhibit narrower ranges of interpretation of the cloudiness terms and less confusion about the meaning of spatial/temporal precipitation modifiers. The study was not intended to be a definitive analysis of public understanding of forecast terminology. Instead, it should be viewed as a primitive form of the type of forecast-terminology study that must be undertaken in the future. Some implications of this investigation for future work in the area are discussed briefly.