Claudio Giuliano

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We propose an approach for extracting relations between entities from biomedical literature based solely on shallow linguistic information. We use a combination of kernel functions to integrate two different information sources: (i) the whole sentence where the relation appears, and (ii) the local contexts around the interacting entities. We performed(More)
In this paper we present a supervised Word Sense Disambiguation methodology, that exploits kernel methods to model sense distinctions. In particular a combination of kernel functions is adopted to estimate independently both syntagmatic and domain similarity. We defined a kernel function, namely the Domain Kernel, that allowed us to plug “external(More)
We present an approach for extracting relations between named entities from natural language documents. The approach is based solely on shallow linguistic processing, such as tokenization, sentence splitting, part-of-speech tagging, and lemmatization. It uses a combination of kernel functions to integrate two different information sources: (i) the whole(More)
This paper summarizes FBK-irst participation at the lexical substitution task of the SEMEVAL competition. We submitted two different systems, both exploiting synonym lists extracted from dictionaries. For each word to be substituted, the systems rank the associated synonym list according to a similarity metric based on Latent Semantic Analysis and to the(More)
We present the first attempt to perform full FrameNet annotation with crowdsourcing techniques. We compare two approaches: the first one is the standard annotation methodology of lexical units and frame elements in two steps, while the second is a novel approach aimed at acquiring frames in a bottom-up fashion, starting from frame element annotation. We(More)
We present an approach to ontology population based on a lexical substitution technique. It consists in estimating the plausibility of sentences where the named entity to be classified is substituted with the ones contained in the training data, in our case, a partially populated ontology. Plausibility is estimated by using Web data, while the(More)
This paper summarizes IRST’s participation in Senseval-3. We participated both in the English allwords task and in some lexical sample tasks (English, Basque, Catalan, Italian, Spanish). We followed two perspectives. On one hand, for the allwords task, we tried to refine the Domain Driven Disambiguation that we presented at Senseval-2. The refinements(More)
This paper investigates the utility of an unsupervised partof-speech (PoS) system in a task oriented way. We use PoS labels as features for different supervised NLP tasks: Word Sense Disambiguation, Named Entity Recognition and Chunking. Further we explore, how much supervised tagging can gain from unsupervised tagging. A comparative evaluation between(More)
We present an approach for semantic relation extraction between nominals that combines shallow and deep syntactic processing and semantic information using kernel methods. Two information sources are considered: (i) the whole sentence where the relation appears, and (ii) WordNet synsets and hypernymy relations of the candidate nominals. Each source of(More)
The authors investigate two publicly available Web knowledge bases, Wikipedia and Yago, in an attempt to leverage semantic information and increase the performance level of a state-of-the-art coreference resolution engine. They extract semantic compatibility and aliasing information from Wikipedia and Yago, and incorporate it into a coreference resolution(More)