The paper introduces a new annotation of discourse relations in the Prague Dependency Treebank (PDT), i.e. the annotation of the so called secondary connectives (mainly multiword phrases like the condition is, that is the reason why, to conclude, this means etc.). Firstly, the paper concentrates on theoretical introduction of these expressions (mainly with respect to primary connectives like an...

Connectives are cohesive devices that signal the relations between clauses and are critical to the construction of a coherent representation of a text’s meaning. We investigated young readers’ knowledge, processing, and comprehension of temporal, causal, and adversative connectives using off-line and on-line tasks. In a cloze task, 10-year-olds were more accurate than 8-year-olds on temporal an...

Existing works indicate that the absence of explicit discourse connectives makes it difficult to recognize implicit discourse relations. In this paper we attempt to overcome this difficulty for implicit relation recognition by automatically inserting discourse connectives between arguments with the use of a language model. Then we propose two algorithms to use these predicted connectives. One i...

A set of propositional connectives is said to be functionally complete if all propositional formulae can be expressed using only connectives from that set. In this paper we give sufficient and necessary conditions for a one-element set of propositional connectives to be functionally complete. These conditions provide a simple and elegant characterization of functionally complete one-element set...

The paper tries to contribute to the general discussion on discourse connectives, concretely to the question whether it is meaningful to distinguish two separate groups of connectives – i.e. “classical” connectives limited to few predefined classes like conjunctions or adverbs (e.g. but) vs. alternative lexicalizations of connectives (i.e. unrestricted expressions and phrases like the reason is...

Implicit discourse relation recognition is difficult due to the absence of explicit discourse connectives between arbitrary spans of text. In this paper, we use language models to predict the discourse connectives between the arguments pair. We present two methods to apply the predicted connectives to implicit discourse relation recognition. One is to use the sense frequency of the specific con...

Discourse relation classification is an important component for automatic discourse parsing and natural language understanding. The performance bottleneck of a discourse parser comes from implicit discourse relations, whose discourse connectives are not overtly present. Explicit discourse connectives can potentially be exploited to collect more training data to collect more data and boost the p...