Information retrieval (IR) has been developed to provide practical solutions to people's need to find the desired information...

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Parallel grammars and parallel treebanks can be a useful method for studying linguistic diversity and commonality. We use this approach to study how arguments to similar predicates are realized across languages. To that end, we formulate formal principles for aligning at phrase and word levels

In this article, we describe a number of indexing experiments based on indexing terms other than simple keywords. These experiments were conducted as one step in validating a linguistically-motivated indexing model. The problem is important but not new. What is new in this approach is the variety

A linguistically motivated approach to indexing, that is the provision of descriptive terms for texts of any kind, is presented and illustrated. The approach is designed to achieve good, i.e. accurate and flexible, indexing by identifying index term sources in the meaning representations built by a

This paper argues that a formal ontology (in our case a description logic knowledge base) should be created in a linguistically motivated way so that it can be queried easily by non-specialists. This can best be achieved by using a strict naming convention that is based on those linguistic

The statistical modelling of language, together with advances in wide-coverage grammar development, have led to high levels of robustness and efficiency in NLP systems and made linguistically motivated

motivated fashion. This paper presents our approach in establishing a framework for semi-automatic linguistic enrichment of ontologies, which led to the development of OntoLing, a plug-in for the popular ontology development tool Protégé. We describe here its features and design aspects which characterize

Abstract. This paper presents a new probabilistic model of information retrieval. The most important modeling assumption made is that documents and queries are defined by an ordered sequence of single terms. This assumption is not made in well known existing models of information retrieval, but is essential in the field of statistical natural language processing. Advances already made in statistical natural language processing will be used in this paper to formulate a probabilistic justification for using tf×idf term weighting. The paper shows that the new probabilistic interpretation of tf×idf term weighting might lead to better understanding of statistical ranking mechanisms, for example by explaining how they relate to coordination level ranking. A pilot experiment on the Cranfield test collection indicates that the presented model outperforms the vector space model with classical tf×idf and cosine length normalisation.

In this paper, we present a supervised classification approach for disambiguation of preposition senses. We use the SemEval 2007 Preposition Sense Disambiguation datasets to evaluate our system and compare its results to those of the systems participating in the workshop. We derived linguistically