EACL

This paper describes the development of a rule-based computational model that describes how a feature-based representation of shared visual information combines with linguistic cues to enable effective reference resolution. This work explores a language-...

Current Named Entity Recognition systems suffer from the lack of hand-tagged data as well as degradation when moving to other domain. This paper explores two aspects: the automatic generation of gazetteer lists from unlabeled data; and the building of a ...

This paper presents a way in which a lexicalised HPSG grammar can handle word order constraints in a computational parsing system, without invoking an additional layer of representation for word order, such as Reape's Word Order Domain. The key proposal ...

Many verbal jokes, like garden path sentences, pose difficulties to models of discourse since the initially primed interpretation needs to be discarded and a new one created based on subsequent statements. The effect of the joke depends on the fact that ...

In the current project, we aim at developing an approach for automatically answering why-questions. We created a data collection for research, development and evaluation of a method for automatically answering why-questions (why-QA) The resulting ...

This paper presents results from experiments in automatic classification of animacy for Norwegian nouns using decision-tree classifiers. The method makes use of relative frequency measures for linguistically motivated morphosyntactic features extracted ...

This paper describes a system that produces extractive summaries of short works of literary fiction. The ultimate purpose of produced summaries is defined as helping a reader to determine whether she would be interested in reading a particular story. To ...

This paper addresses the problem of automatically retrieving answers for how-to questions, focusing on those that inquire about the procedure for achieving a specific goal. For such questions, typical information retrieval methods, based on key word ...

Metonymy recognition is generally approached with complex algorithms that rely heavily on the manual annotation of training and test data. This paper will relieve this complexity in two ways. First, it will show that the results of the current learning ...