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We address the task of computing vector space representations for the meaning of word occurrences , which can vary widely according to context. This task is a crucial step towards a robust, vector-based compositional account of sentence meaning. We argue that existing models for this task do not take syntactic structure sufficiently into account. We present(More)
This paper describes the SALSA corpus, a large German corpus manually annotated with role-semantic information, based on the syntactically annotated TIGER newspaper corpus (Brants et al., 2002). The first release, comprising about 20,000 annotated predicate instances (about half the TIGER corpus), is scheduled for mid-2006. In this paper we discuss the(More)
This task consists of recognizing words and phrases that evoke semantic frames as defined in the FrameNet project (http: //framenet.icsi.berkeley.edu), and their semantic dependents, which are usually, but not always, their syntactic dependents (including subjects). The training data was FN annotated sentences. In testing, participants automatically(More)
In this paper, we consider the computational modelling of human plausibility judgements for verb-relation-argument triples, a task equivalent to the computation of selectional preferences. Such models have applications both in psycholinguistics and in computational linguistics. By extending a recent model, we obtain a completely corpus-driven model for this(More)
The constraint language for lambda structures (CLLS) is an expressive language of tree descriptions which combines dominance constraints with powerful parallelism and binding constraints. CLLS was introduced as a uniform framework for defining underspecified semantics representations of natural language sentences, covering scope, ellipsis, and anaphora.(More)
We present the first large-scale English " all-words lexical substitution " corpus. The size of the corpus provides a rich resource for investigations into word meaning. We investigate the nature of lexical substitute sets, comparing them to WordNet synsets. We find them to be consistent with, but more fine-grained than, synsets. We also identify(More)