Rumen Moraliyski


2014

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HulTech: A General Purpose System for Cross-Level Semantic Similarity based on Anchor Web Counts
Jose G. Moreno | Rumen Moraliyski | Asma Berrezoug | Gaël Dias
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Recognize the Generality Relation between Sentences Using Asymmetric Association Measures
Sebastiao Pais | Gael Dias | Rumen Moraliyski
Proceedings of the First International Conference on Computational Linguistics in Bulgaria (CLIB 2014)

In this paper we focus on a particular case of entailment, namely entailment by generality. We argue that there exist various types of implication, a range of different levels of entailment reasoning, based on lexical, syntactic, logical and common sense clues, at different levels of difficulty. We introduce the paradigm of Textual Entailment (TE) by Generality, which can be defined as the entailment from a specific statement towards a relatively more general statement. In this context, the Text T entails the Hypothesis H, and at the same time H is more general than T . We propose an unsupervised and language-independent method to recognize TE by Generality given a case of Text − Hypothesis or T − H where entailment relation holds.

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Unsupervised and Language Independent Method to Recognize Textual Entailment by Generality
Sebastiao Pais | Gael Dias | Joao Cordeiro | Rumen Moraliyski
Proceedings of the First International Conference on Computational Linguistics in Bulgaria (CLIB 2014)

In this work we introduce a particular case of textual entailment (TE), namely Textual Entailment by Generality (TEG). In text, there are different kinds of entailment yielded from different types of implicative reasoning (lexical, syntactic, common sense based), but here we focus just on TEG, which can be defined as an entailment from a specific statement towards a relatively more G general one. Therefore, we have T (G)→ H whenever the premise T entails the hypothesis H, the hypothesis being more general than the premise. We propose an unsupervised and language-independent method to recognize TEGs, given a pair T, H in an entailment relation. We have evaluated our proposal G → H English pairs, where we know through two experiments: (a) Test on T (G)→ H English pairs, where we know that TEG holds; (b) Test on T → H Portuguese pairs, randomly selected with 60% of TEGs and 40% of TE without generality dependency (TEnG).

2010

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Paraphrase Alignment for Synonym Evidence Discovery
Gintarė Grigonytė | João Paulo Cordeiro | Gaël Dias | Rumen Moraliyski | Pavel Brazdil
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)