A Psycholinguistic Model for the Marking of Discourse Relations

Discourse relations can either be explicitly marked by discourse connectives (DCs), such as therefore and but, or implicitly conveyed in natural language utterances. How speakers choose between the two options is a question that is not well understood. In this study, we propose a psycholinguistic model that predicts whether or not speakers will produce an explicit marker given the discourse relation they wish to express. Our model is based on two information-theoretic frameworks: (1) the Rational Speech Acts model, which models the pragmatic interaction between language production and interpretation by Bayesian inference, and (2) the Uniform Information Density theory, which advocates that speakers adjust linguistic redundancy to maintain a uniform rate of information transmission. Specifically, our model quantifies the utility of using or omitting a DC based on the expected surprisal of comprehension, cost of production, and availability of other signals in the rest of the utterance. Experiments based on the Penn Discourse Treebank show that our approach outperforms the state-of-the-art performance at predicting the presence of DCs (Patterson and Kehler, 2013), in addition to giving an explanatory account of the speaker’s choice

Dabei in der gegenwärtigen deutschen Lexikographie und Grammatikschreibung. Wie vergleichstauglich s

Apartir de dos rasgos estructurales definitorios del alemán contemporáneo, por un lado la combinación de mecanismos de conexión discursivos (anáfora) y sintácticos (conjunción subordinante) y, por otro, la posibilidad de formación de un Mittelfeld, se discute la validez de algunos de los conceptos y de la terminología empleados para la descripción de los valores del conector dabei (localidad-temporalidad, adición, contraste, continuación) en alemán contemporáneo. Del trato recibido por dabei en la gramaticografía y lexicografía del alemán contemporáneo se desprenden una serie de condiciones para la adecuada descripción de dabei en sintaxis supraoracional y en gramática textual con fines comparativos

The Automatic Acquisition of Knowledge about Discourse Connectives

Institute for Communicating and Collaborative SystemsThis thesis considers the automatic acquisition of knowledge about discourse connectives. It focuses in particular on their semantic properties, and on the relationships that hold between them. There is a considerable body of theoretical and empirical work on discourse connectives. For example, Knott (1996) motivates a taxonomy of discourse connectives based on relationships between them, such as HYPONYMY and EXCLUSIVE, which are defined in terms of substitution tests. Such work requires either great theoretical insight or manual analysis of large quantities of data. As a result, to date no manual classification of English discourse connectives has achieved complete coverage. For example, Knott gives relationships between only about 18% of pairs obtained from a list of 350 discourse connectives. This thesis explores the possibility of classifying discourse connectives automatically, based on their distributions in texts. This thesis demonstrates that state-of-the-art techniques in lexical acquisition can successfully be applied to acquiring information about discourse connectives. Central to this thesis is the hypothesis that distributional similarity correlates positively with semantic similarity. Support for this hypothesis has previously been found for word classes such as nouns and verbs (Miller and Charles, 1991; Resnik and Diab, 2000, for example), but there has been little exploration of the degree to which it also holds for discourse connectives. We investigate the hypothesis through a number of machine learning experiments. These experiments all use unsupervised learning techniques, in the sense that they do not require any manually annotated data, although they do make use of an automatic parser. First, we show that a range of semantic properties of discourse connectives, such as polarity and veridicality (whether or not the semantics of a connective involves some underlying negation, and whether the connective implies the truth of its arguments, respectively), can be acquired automatically with a high degree of accuracy. Second, we consider the tasks of predicting the similarity and substitutability of pairs of discourse connectives. To assist in this, we introduce a novel information theoretic function based on variance that, in combination with distributional similarity, is useful for learning such relationships. Third, we attempt to automatically construct taxonomies of discourse connectives capturing substitutability relationships. We introduce a probability model of taxonomies, and show that this can improve accuracy on learning substitutability relationships. Finally, we develop an algorithm for automatically constructing or extending such taxonomies which uses beam search to help find the optimal taxonomy