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The present paper presents a multimodel inference approach to linguistic variation, expanding on prior work by Kuperman and Bresnan (2012). We argue that corpus data often present the analyst with high model selection uncertainty. This uncertainty is inevitable given that language is highly redundant: every feature is predictable from multiple other features. However, uncertainty involved in model selection is ignored by the standard method of selecting the single best model and inferring the effects of the predictors under the assumption that the best model is true. Multimodel inference avoids committing to a single model. Rather, we make predictions based on the entire set of plausible models, with contributions of models weighted by the models' predictive value. We argue that multimodel inference is superior to model selection for both the I-Language goal of inferring the mental grammars that generated the corpus, and the E-Language goal of predicting characteristics of future speech samples from the community represented by the corpus. Applying multimodel inference to the classic problem of English auxiliary contraction, we show that the choice between multimodel inference and model selection matters in practice: the best model may contain predictors that are not significant when the full set of plausible models is considered, and may omit predictors that are significant considering the full set of models. We also contribute to the study of English auxiliary contraction. We document the effects of priming, contextual predictability, and specific syntactic constructions and provide evidence against effects of phonological context.

This article provides a preliminary semantic framework for Dependency Grammar in which lexical words are semantically defined as contextual distributions (sets of contexts) while syntactic dependencies are compositional operations on word distributions. More precisely, any syntactic dependency uses the contextual distribution of the dependent word to restrict the distribution of the head, and makes use of the contextual distribution of the head to restrict that of the dependent word. The interpretation of composite expressions and sentences, which are analyzed as a tree of binary dependencies, is performed by restricting the contexts of words dependency by dependency in a left-to-right incremental way. Consequently, the meaning of the whole composite expression or sentence is not a single representation, but a list of contextualized senses, namely the restricted distributions of its constituent (lexical) words. We report the results of two large-scale corpus-based experiments on two different natural language processing applications: paraphrasing and compositional translation.

Shanghainese is an extremely topic-prominent language with many topic markers in competition with one another, often without any obvious basis for the selection of one topic marker over another. We explore the influence of five variables on the five most frequent topic markers in a corpus of (spoken) Shanghainese: topic length, syntactic category of the topic, function of the topic, comment type, and genre. We carry out a multivariate statistical analysis of the data, relying on a polytomous logistic regression model. Our approach leads to a satisfying quantification of the role of each factor, as well as an estimate of the probabilities of combinations of factors, in influencing the choice of topic marker. This study serves simultaneously as an introduction to the polytomous package (Arppe 2013) in the statistical software package R.

In order to investigate the distinct nuances of meaning conveyed by the different intonational contours encountered in yes-no questions in English, we conducted a corpus study of the intonation of 410 naturally occurring spoken interrogative-form yes-no questions in American English. First we annotated the intonation of each question using ToBI and then examined the meaning of each utterance in the context. We found that the low-rise nuclear contour (e.g., L*H-H%) is the unmarked question contour and is by far the most frequently occurring. Yes-no questions with falling intonation (e.g. H*L-L%) do not occur frequently, but when they do, they can be classified in speech act terms as “non-genuine” questions, where one or more felicity conditions on genuine questions are not met. Level questions (e.g., L*H-L%) tend to be “stylized” in meaning and pattern with falling questions in being non-genuine. We also found that the pitch accent on high-rise questions (e.g., H*H-H%), where the final pitch contour starts high and ends higher, tends to mark information that is given in the discourse or a function word. These are syllables that would normally remain unaccented parts of the post-nuclear “tail” of the intonation phrase. This leads us to propose that many such accents are “post-nuclear accents” in the sense of Ladd 2008.

Cross-linguistically, both subjects and topical information tend to be placed at the beginning of a sentence. Subjects are generally highly topical, causing both tendencies to converge on the same word order. However, subjects that lack prototypical topic properties may give rise to an incongruence between the preference to start a sentence with the subject and the preference to start a sentence with the most accessible information. We present a corpus study in which we investigate in what syntactic position (preverbal or postverbal) such low-accessible subjects are typically found in Dutch natural language. We examine the effects of both discourse accessibility (definiteness) and inherent accessibility (animacy). Our results show that definiteness and animacy interact in determining subject position in Dutch. Non-referential (bare) subjects are less likely to occur in preverbal position than definite subjects, and this tendency is reinforced when the subject is inanimate. This suggests that these two properties that make the subject less accessible together can ‘gang up’ against the subject first preference. The results support a probabilistic multifactorial account of syntactic variation.

Discourse connectives are lexical items indicating coherence relations between discourse segments. Even though many languages possess a whole range of connectives, important divergences exist cross-linguistically in the number of connectives that are used to express a given relation. For this reason, connectives are not easily paired with a univocal translation equivalent across languages. This paper is a first attempt to design a reliable method to annotate the meaning of discourse connectives cross-linguistically using corpus data. We present the methodological choices made to reach this aim and report three annotation experiments using the framework of the Penn Discourse Tree Bank.