We describe an annotation experiment combining topics from lexicography and Word Sense Disambiguation. It involves a lexicon (Pattern Dictionary of English Verbs, PDEV), an existing data set (VPS-GradeUp), and an unpublished data set (RTE in PDEV Implicatures). The aim of the experiment was twofold: a pilot annotation of Recognizing Textual Entailment (RTE) on PDEV implicatures (lexicon glosses...

This paper presents a method of unsupervised word sense discrimination that augments co–occurrence feature vectors derived from raw untagged corpora with information from the glosses found in a machine readable dictionary. Each content word that occurs in the context of a target word to be discriminated is represented by a co-occurrence feature vector. Each of these vectors is augmented with th...

In the present study, introductory-level German students read a simplified story and learned the meanings of new German words by reading English translations in marginal glosses versus trying to infer (i.e., guess) their translations. Students who inferred translations were given feedback in English or in German, or no feedback at all. Although immediate retention of new vocabulary was better f...

A critical prerequisite for human-level cognitive systems is having a rich conceptual understanding of the world. We describe a system that learns conceptual knowledge by deep understanding of WordNet glosses. While WordNet is often criticized for having a too fine-grained approach to word senses, the set of glosses do generally capture useful knowledge about the world and encode a substantial ...

In this paper we present a progress report of the OntoWordNet project, a research program aimed at achieving a formal specification of WordNet. Within this program, we developed a hybrid bottom-up top-down methodology to automatically extract association relations from WordNet, and to interpret those associations in terms of a set of conceptual relations, formally defined in the DOLCE foundatio...

This document describes the Word Sense Disambiguation system used by Language Computer Corporation at English Coarse Grained All Word Task at SemEval 2007. The system is based on two supervised machine learning algorithms: Maximum Entropy and Support Vector Machines. These algorithms were trained on a corpus created from SemCor, Senseval 2 and 3 all words and lexical sample corpora and Open Min...

This paper describes a case study on methods for automatically extracting qualia relations from dictionary glosses in Italian, namely the Senso Comune De Mauro Dictionary (SCDM). The qualia extraction has been addressed by means of a pattern-based approach and lexical match with an Italian generative lexicon based language resource, PAROLE-SIMPLECLIPS (PSC). The evaluation of the extraction app...