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Coherence relations can be made linguistically explicit by means of connectives (e.g., but, because) or cue phrases (e.g., on the other hand, which is why), but can also be left implicit and conveyed through the juxtaposition of two clauses or sentences. However, it seems that not all relations are equally easy to reconstruct when they are implicit. In this paper, we explore which features of coherence relations make them more, or less, likely to be conveyed implicitly. We adopt the assumption that expected relations are more often implicit than relations that are not expected, and propose to determine a relation's expectedness using the notion of cognitive complexity. We test our hypotheses by means of a parallel corpus study, in which we analyze the translations of explicit English coherence relations from the Europarl Direct corpus into four target languages: Dutch, German, French, and Spanish. We find that cognitive complexity indeed influences the linguistic marking of coherence relations, and that this does not vary between the languages in our corpus. In addition, we find that a relation's relational and syntactic dependency also influences its linguistic marking, but that these measures are not completely independent of relation type.

In this paper, we show how three often used and seemingly different discourse annotation frameworks – Penn Discourse Treebank (PDTB), Rhetorical Structure Theory (RST), and Segmented Discourse Representation Theory – can be related by using a set of unifying dimensions. These dimensions are taken from the Cognitive approach to Coherence Relations and combined with more fine-grained additional features from the frameworks themselves to yield a posited set of dimensions that can successfully map three frameworks. The resulting interface will allow researchers to find identical or at least closely related relations within sets of annotated corpora, even if they are annotated within different frameworks. Furthermore, we tested our unified dimension (UniDim) approach by comparing PDTB and RST annotations of identical newspaper texts and converting their original end label annotations of relations into the accompanying values per dimension. Subsequently, rates of overlap in the attributed values per dimension were analyzed. Results indicate that the proposed dimensions indeed create an interface that makes existing annotation systems “talk to each other.”

Psycholinguistic investigations of the way readers and speakers perceive gender have shown several biases associated with how gender is linguistically realized in language. Although such variations across languages offer interesting grounds for legitimate cross-linguistic comparisons, pertinent characteristics of grammatical systems – especially in terms of their gender asymmetries – have to be clearly identified. In this paper, we present a language index for researchers interested in the effect of grammatical gender on the mental representations of women and men. Our index is based on five main language groups (i.e., grammatical gender languages, languages with a combination of grammatical gender and natural gender, natural gender languages, genderless languages with few traces of grammatical gender and genderless languages) and three sets of specific features (morphology, masculine-male generics and asymmetries). Our index goes beyond existing ones in that it provides specific dimensions relevant to those interested in psychological and sociological impacts of language on the way we perceive women and men. We also offer a critical discussion of any endeavor to classify languages according to grammatical gender.

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