Categories and classifications in EuroWordNet

In EuroWordNet we develop wordnets in 8 European languages, which are structured along the same lines as the Princeton WordNet. The wordnets are inter-linked in a multilingual database, where they can be compared. This comparison reveals many different lexicalizations of classes across the languages that also lead to important differences in the hierarchical structure of the wordnets. It is not feasible to include all these classes (the superset) in each language-specific wordnet and to reach consensus on the implicational effects across all the languages. Each wordnet is therefore limited to the lexicalized words and expressions of a language. The wordnets are thus autonomous language-specific structures that capture valuable information about the lexicalization of each language, which is important for information retrieval, machine translation and language generation. By connecting the wordnets to a separate ontology, semantic inferencing can still be guaranteed. Still, different types of classification schemes can be distinguished among the lexicalized classes. In this paper we will further describe the properties of these different classes and discuss the advantages and effects of distinguishing them in wordnet-like structures

SemEval-2018 Task 5: Counting Events and Participants in the Long Tail

This paper discusses SemEval-2018 Task 5: a referential quantification task of counting events and participants in local, long-tail news documents with high ambiguity. The complexity of this task challenges systems to establish the meaning, reference and identity across documents. The task consists of three subtasks and spans across three domains. We detail the design of this referential quantification task, describe the participating systems, and present additional analysis to gain deeper insight into their performance

A Distributed Database System for Developing Ontological and Lexical Resources in Harmony

Abstract. In this article, we present the basic ideas of creating a new information-rich lexical database of Dutch, called Cornetto, that is interconnected with corresponding English synsets and a formal ontology. The Cornetto database is based on two existing electronic dictionaries -the Referentie Bestand Nederlands (RBN) and the Dutch wordnet (DWN). The former holds FrameNet-like information for Dutch and the latter is structured as the English wordnet. In Cornetto, three different collections are maintained for lexical units, synsets and ontology terms. The database interlinks the three collections and aims at clarifying the relations between them. The organization and work processes of the project are briefly introduced. We also describe the design and implementation of new tools prepared for the lexicographic work on the Cornetto project. The tools are based on the DEB development platform and behave as special dictionary clients for the well-known DEBVisDic wordnet editor and browser

Scoring and Classifying Implicit Positive Interpretations:A Challenge of Class Imbalance

This paper reports on a reimplementation of a system on detecting implicit positive meaning from negated statements. In the original regression experiment, different positive interpretations per negation are scored according to their likelihood. We convert the scores to classes and report our results on both the regression and classification tasks. We show that a baseline taking the mean score or most frequent class is hard to beat because of class imbalance in the dataset. Our error analysis indicates that an approach that takes the information structure into account (i.e. which information is new or contrastive) may be promising, which requires looking beyond the syntactic and semantic characteristics of negated statements

Meaningful results for Information Retrieval in the MEANING project

The goal of the MEANING project (IST-2001-34460) is to develop tools for the automatic acquisition of lexical knowledge that will help Word Sense Disambiguation (WSD). The acquired lexical knowledge from various sources and various languages is stored in the Multilingual Central Repository (MCR) (Atserias et al 04), which is based on the design of the EuroWordNet database. The MCR holds wordnets in various languages (English, Spanish, Italian, Catalan and Basque), which are interconnected via an Inter-Lingual-Index (ILI). In addition, the MCR holds a number of ontologies and domain labels related to al