Our contribution tackles the problem of learning to understand anaphoric references in the context of robotic machine learning; e.g. Get the large screw Put it in the left hole. Our solution assumes the probabilistic theory of learning spelt out in earlier publications. Associations are formed probabilistically between constituents of the verbal command and constituents of a presupposed in...

This paper reports on the SYN-RA (SYNtax-based Reference Annotation) project, an on-going project of annotating German newspaper texts with referential relations. The project has developed an inventory of anaphoric and coreference relations for German in the context of a unified, XML-based annotation scheme for combining morphological, syntactic, semantic, and anaphoric information. The paper d...

Indefinite noun phrases in certain contexts are unable to support anaphoric coreference to an individual entity, and therefore should be ignored when searching for coreferent antecedents of anaphoric pronouns. However, many algorithms for anaphora resolution utilize noun phrase chunking or shallow parsing, and therefore do not make the needed distinctions to avoid this type of spurious antecede...

Two experiments investigated whether the choice of anaphoric expression is affected by the presence of an addressee. Following a context sentence and visual scene, participants described a target scene that required anaphoric reference. They described the scene either to an addressee (Experiment 1) or without an addressee (Experiment 2). When an addressee was present in the task, participants u...

Biomedical literature has been the focus of relevant information extraction projects, however there is no corpus of full scientific articles annotated with anaphoric links for training and evaluation of anaphora resolution systems—which are an important part of information extraction efforts—for this domain. We have created a corpus of biomedical articles that are annotated with anaphoric links...

It is widely held that children's linguistic input underdetermines the correct grammar, and that language learning must therefore be guided by innate linguistic constraints. Here, we show that a Bayesian model can learn a standard poverty-of-stimulus example, anaphoric one, from realistic input by relying on indirect evidence, without a linguistic constraint assumed to be necessary. Our demonst...