Any attempt to characterize valency patterns within and across languages confronts the phenomenon of verb or valency classes: sets of semantically-related verbs sharing a range of linguistic properties, such as the possible realizations of their arguments and the particular interpretation associated with each possible argument realization. The Leipzig Valency Classes Project provides an excelle...

In this paper, we propose innovative representations for automatic classification of verbs according to mainstream linguistic theories, namely VerbNet and FrameNet. First, syntactic and semantic structures capturing essential lexical and syntactic properties of verbs are defined. Then, we design advanced similarity functions between such structures, i.e., semantic tree kernel functions, for exp...

This paper reports the results of Natural Language Processing (NLP) experiments in semantic parsing, based on a new semantic resource, the Pattern Dictionary of English Verbs (PDEV) (Hanks, 2013). This work is set in the DVC (Disambiguating Verbs by Collocation) project aimed at expanding PDEV to a large scale. This project springs from a long-term collaboration of lexicographers with computer ...

This paper implements a simple vector space model relying on lexico-syntactic patterns to distinguish between the paradigmatic relations synonymy, antonymy and hypernymy. Our study is performed across word classes, and models the lexical relations between German nouns, verbs and adjectives. Applying nearest-centroid classification to the relation vectors, we achieve a precision of 59.80%, which...

The aim of our research is to develop a scalable automatic why question answering system for English based on supervised method that uses part of speech analysis. The prior approach consisted in building a why-classifier using function words. This paper investigates the performance of combining supervised data mining methods with various feature selection strategies in order to obtain a more ac...

The acquisition of argument structures has been studied by a variety of second language acquisition scholars within the past two decades (Atay, 2010; Can, 2009; Chay, 2006, & Kras, 2007, among others). In the present study, ‘Predicate’ as the most substantial element of a sentence is investigated. There are three English verb-types which seem to be more problematic for Persian EFL learners: (a)...

This article addresses the causal structure of events described by verbs: whether an event happens spontaneously or it is caused by an external causer. We automatically estimate the likelihood of external causation of events based on the distribution of causative and anticausative uses of verbs in the causative alternation. We train a Bayesian model and test it on a monolingual and on a bilingu...

We infer semantic classes of verbs signaling modality from a purely syntactic classification of 637 German verbs by applying findings from linguistics about correspondences between verb meaning and syntax. Our extensive evaluation of the semantic classification is based on a linking to three other lexical resources at the word sense level: to the German wordnet GermaNet and to the English resou...

The choice of verb features is crucial for the learning of verb classes. This paper presents clustering experiments on 168 German verbs, which explore the relevance of features on three levels of verb description, purely syntactic frame types, prepositional phrase information and selectional preferences. In contrast to previous approaches concentrating on the sparse data problem, we present evi...

This paper presents a token-based automatic classification of German perception verbs into literal vs. multiple non-literal senses. Based on a corpus-based dataset of German perception verbs and their systematic meaning shifts, we identify one verb of each of the four perception classes optical, acoustic, olfactory, haptic, and use Decision Trees relying on syntactic and semantic corpus-based f...