This paper provides a binary, token-based classification of German particle verbs (PVs) into literal vs. non-literal usage. A random forest improving standard features (e.g., bagof-words; affective ratings) with PV-specific information and abstraction over common nouns significantly outperforms the majority baseline. In addition, PV-specific classification experiments demonstrate the role of sh...

Detailed knowledge about verbs is critical in many NLP and IR tasks, yet manual determination of such knowledge for large numbers of verbs is difficult, time-consuming and resource intensive. Recent responses to this problem have attempted to classify verbs automatically, as a first step to automatically build lexical resources. In order to estimate the upper bound of a verb classification task...

We describe a state-of-the-art automatic system that can acquire subcategorisation frames from raw text for a free word-order language. We use it to construct a subcategorisation lexicon of German verbs from a large Web page corpus. With an automatic verb classification paradigm we evaluate our subcategorisation lexicon against a previous classification of German verbs; the lexicon produced by ...

The automatic disambiguation of verbs in domain independent text becomes more and more important for applications such as Machine Translation, Text Summarization, and Question Answering, mainly because verbs play a key factor in the syntactic and semantic interpretation of sentences. In this paper we present a system for the automatic classification of token verbs in context based on VerbNet cl...

Lexical classifications have proved useful in supporting various natural language processing (NLP) tasks. The largest verb classification for English is Levin’s (1993) work which defined groupings of verbs based on syntactic and semantic properties. VerbNet (Kipper et al., 2000; Kipper-Schuler, 2005) – the largest computational verb lexicon currently available for English – provides detailed sy...

Lexical classes, when tailored to the application and domain in question, can provide an effective means to deal with a number of natural language processing (NLP) tasks. While manual construction of such classes is difficult, recent research shows that it is possible to automatically induce verb classes from cross-domain corpora with promising accuracy. We report a novel experiment where simil...

In this paper, we introduce a tripartite scheme for the classification of intransitive verbs for Hindi and claim it to be a more suitable model of classification than the classical binary unaccusative/unergative classification. We develop a multi-class SVM classifier based model for automatic classification of intransitive verbs into proposed tripartite classes. We rank the unaccusative diagnos...

The current study works at the interface of theoretical and computational linguistics to explore the semantic properties of an particle verbs, i.e., German particle verbs with the particle an. Based on a thorough analysis of the particle verbs from a theoretical point of view, we identified empirical features and performed an automatic semantic classification. A focus of the study was on the mu...

Lexical classifications have proved useful in supporting various natural language processing (NLP) tasks. The largest verb classification for English is Levin’s (1993) work which defined groupings of verbs based on syntactic properties. VerbNet (Kipper et al., 2000; Kipper-Schuler, 2005) – the largest computational verb lexicon currently available for English – provides detailed syntactic-seman...