We describe here a procedure to combine two different existing techniques for Ontology Enrichment with domain-specific concepts. The resulting algorithm is fully unsupervised, and the level of precision is higher than when they are used separately, so we believe that both algorithms benefit from each other. The experiments have been performed by extending WordNet with concepts extracted from Th...

In this paper we show a general advance about the desgin of a methodology for extracting definitional contexts from corpus of biomedicine in Spanish, taking into account a set of processes performed by the following modules: (i) a term extractor based in a hybrid method, (ii) a set of verbs that configure the syntactic structure of a definitional context, (iii) a chunker able to recognize those...

This work investigates the variation in a word’s distributionally nearest neighbours with respect to the similarity measure used. We identify one type of variation as being the relative frequency of the neighbour words with respect to the frequency of the target word. We then demonstrate a three-way connection between relative frequency of similar words, a concept of distributional gnerality an...

In this paper we present the approach of introducing thesaurus knowledge into probabilistic topic models. The main idea of the approach is based on the assumption that the frequencies of semantically related words and phrases, which are met in the same texts, should be enhanced: this action leads to their larger contribution into topics found in these texts. We have conducted experiments with s...

In this study, we measure the contribution of different event components and particular semantic relations to the task of event coreference resolution. First we calculate what event times, locations and participants add to event coreference resolution. Secondly, we analyze the contribution by hyponymy and granularity within the participant component. Coreference of events is then calculated fro...

We describe a system proposed for measuring the degree of relational similarity beetwen a pair of words at the Task #2 of Semeval 2012. The approach presented is based on a vectorial representation using the following features: i) the context surrounding the words with a windows size = 3, ii) knowledge extracted from WordNet to discover several semantic relationships, such as meronymy, hyponymy...

This paper proposes an efficient online method that trains a classifier with many conjunctive features. We employ kernel computation called kernel slicing, which explicitly considers conjunctions among frequent features in computing the polynomial kernel, to combine the merits of linear and kernel-based training. To improve the scalability of this training, we reuse the temporal margins of part...

This paper describes how a concept lattice that represents semantic relations (synonymy, hyponymy, hypernymy) in a set of words can be used for semantic matching. This kind of concept lattice is the result of the formal concept analysis technique used for determining semantic relations in a set of words extracted from a monolingual dictionary. It is shown how relations between concepts can be m...

The integration of linguistic and domain-oriented knowledge resources is a common task in many current projects with NLP objectives. WordNet and EuroWordNet offer large lexical resources for such purposes. Nevertheless, their exploitation in conjunction with specialized knowledge is still a non-trivial task. We propose to use fuzzy ontologies, which combine Fuzzy Logic theory and Description Lo...

We present a corpus-based supervised learning system for coarse-grained sense disambiguation. In addition to usual features for training in word sense disambiguation, our system also uses Base Level Concepts automatically obtained from WordNet. Base Level Concepts are some synsets that generalize a hyponymy sub–hierarchy, and provides an extra level of abstraction as well as relevant informatio...