Rumen Moraliyski

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We describe a new unsupervised approach for synonymy discovery by aligning paraphrases in monolingual domain corpora. For that purpose, we identify phrasal terms that convey most of the concepts within domains and adapt a methodology for the automatic extraction and alignment of paraphrases to identify paraphrase casts from which valid synonyms are(More)
Thesauri, that list the most salient semantic relations between words have mostly been compiled manually. Therefore, the inclusion of an entry depends on the subjective decision of the lexicographer. As a consequence, those resources are usually incomplete. In this paper, we propose an unsupervised methodology to automatically discover pairs of semantically(More)
Until now, Natural Language Processing (NLP) research development has mainly been conducted for the English speaking community. However, the European Union with its 25 member-states already involves 22 different official languages. As a consequence, multilinguality is certainly the most important challenge of this century for the European NLP community. In(More)
This paper describes the HULTECH team participation in Task 3 of SemEval-2014. Four different subtasks are provided to the participants, who are asked to determine the semantic similarity of cross-level test pairs: paragraphto-sentence, sentence-to-phrase, phrase-toword and word-to-sense. Our system adopts a unified strategy (general purpose system) to(More)
In this paper, we present a new methodology for synonym detection based on the combination of global and local distributional similarities of pairs of words. The methodology is evaluated on the noun space of the 50 multiple-choice synonym questions taken from the ESL and reaches 91.30% accuracy using a conditional probabilistic model associated with the(More)
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