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This paper presents a novel deterministic algorithm for implicit Semantic Role Labeling. The system exploits a very simple but relevant discursive property, the argument coherence over different instances of a predicate. The algorithm solves the implicit arguments sequentially, exploiting not only explicit but also the implicit arguments previously solved.(More)
This paper presents the complete and consistent ontological annotation of the nominal part of WordNet. The annotation has been carried out using the semantic features defined in the EuroWordNet Top Concept Ontology and made available to the NLP community. Up to now only an initial core set of 1,024 synsets, the so-called Base Concepts, was ontologized in(More)
This paper presents a novel automatic approach to partially integrate FrameNet and WordNet. In that way we expect to extend FrameNet coverage, to enrich WordNet with frame semantic information and possibly to extend FrameNet to languages other than English. The method uses a knowledge-based Word Sense Disambiguation algorithm for linking FrameNet lexical(More)
This paper presents the Predicate Matrix v1.1, a new lexical resource resulting from the integration of multiple sources of predicate information including FrameNet (Baker et al. We start from the basis of SemLink. Then, we use advanced graph-based algorithms to further extend the mapping coverage of SemLink. Second, we also exploit the current content of(More)
—Following the frame semantics paradigm, we present a novel strategy for solving null-instantiated arguments. Our method learns probability distributions of semantic types for each Frame Element from explicit corpus annotations. These distributions are used to select the most probable missing implicit arguments together with its most probable fillers. We(More)
This paper investigates the contribution of document level processing of time-anchors for TimeLine event extraction. We developed and tested two different systems. The first one is a baseline system that captures explicit time-anchors. The second one extends the baseline system by also capturing implicit time relations. We have evaluated both approaches in(More)
With the proliferation of applications sharing information represented in multiple ontologies, the development of automatic methods for robust and accurate ontology matching will be crucial to their success. Connecting and merging already existing semantic networks is perhaps one of the most challenging task related to knowledge engineering. This paper(More)