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The Conference on Computational Natural Language Learning is accompanied every year by a shared task whose purpose is to promote natural language processing applications and evaluate them in a standard setting. In 2008 the shared task was dedicated to the joint parsing of syntactic and semantic dependencies. This shared task not only unifies the shared(More)
For the 11th straight year, the Conference on Computational Natural Language Learning has been accompanied by a shared task whose purpose is to promote natural language processing applications and evaluate them in a standard setting. In 2009, the shared task was dedicated to the joint parsing of syntactic and semantic dependencies in multiple languages.(More)
This paper presents our contribution in the closed track of the 2008 CoNLL Shared Task (Surdeanu et al., 2008). To tackle the problem of joint syntactic–semantic analysis , the system relies on a syntactic and a semantic subcomponent. The syntactic model is a bottom-up projective parser using pseudo-projective transformations, and the semantic model uses(More)
Fine-grained opinion analysis methods often make use of linguistic features but typically do not take the interaction between opinions into account. This article describes a set of experiments that demonstrate that relational features, mainly derived from dependency-syntactic and semantic role structures, can significantly improve the performance of(More)
Almost all automatic semantic role labeling (SRL) systems rely on a preliminary parsing step that derives a syntactic structure from the sentence being analyzed. This makes the choice of syntactic representation an essential design decision. In this paper, we study the influence of syntactic representation on the performance of SRL systems. Specifically, we(More)
We demonstrate that relational features derived from dependency-syntactic and semantic role structures are useful for the task of detecting opinionated expressions in natural-language text, significantly improving over conventional models based on sequence labeling with local features. These features allow us to model the way opinionated expressions(More)