Connectives and Causal Relatedness in Expository Text

The combined influence of causal connectives and varying levels of causal relatedness on online and offline comprehension of expository texts was investigated. The current hypothesis was that causal connectives would encourage elaborative processing and online generation of bridging inferences, particularly in the moderately to low causally related sentence pairs. This was predicted because past research has noted causal connectives capacity to elicit generation of inferences online. Connectives were not predicted to encourage inference generation in the higher-related items because the causal bridging inference should be obvious to the reader. To test this, a new set of 24 expository items was generated with four versions of each item representing varying levels of causal relatedness. Results show little evidence supporting the hypotheses. To account for the null findings, the potential difficulty and participants lack of familiarity with the text content are discussed as factors. Suggestions for future research are presented

Some Logical Notations for Pragmatic Assertions

The pragmatic notion of assertion has an important inferential role in logic. There are also many notational forms to express assertions in logical systems. This paper reviews, compares and analyses languages with signs for assertions, including explicit signs such as Frege’s and Dalla Pozza’s logical systems and implicit signs with no specific sign for assertion, such as Peirce’s algebraic and graphical logics and the recent modification of the latter termed Assertive Graphs. We identify and discuss the main ‘points’ of these notations on the logical representation of assertions, and evaluate their systems from the perspective of the philosophy of logical notations. Pragmatic assertions turn out to be useful in providing intended interpretations of a variety of logical systems

Merging fragments of classical logic

We investigate the possibility of extending the non-functionally complete logic of a collection of Boolean connectives by the addition of further Boolean connectives that make the resulting set of connectives functionally complete. More precisely, we will be interested in checking whether an axiomatization for Classical Propositional Logic may be produced by merging Hilbert-style calculi for two disjoint incomplete fragments of it. We will prove that the answer to that problem is a negative one, unless one of the components includes only top-like connectives.Comment: submitted to FroCoS 201

Is game semantics necessary?

We discuss the extent to which game semantics is implicit in the formalism of linear logic and in the intuitions underlying linear logic

The online processing of causal and concessive discourse connectives

While there is a substantial amount of evidence for language processing being a highly incremental and predictive process, we still know relatively little about how top-down discourse based expectations are combined with bottom-up information such as discourse connectives. The present article reports on three experiments investigating this question using different methodologies (visual world paradigm and ERPs) in two languages (German and English). We find support for highly incremental processing of causal and concessive discourse connectives, causing anticipation of upcoming material. Our visual world study shows that anticipatory looks depend on the discourse connective; furthermore, the German ERP study revealed an N400 effect on a gender-marked adjective preceding the target noun, when the target noun was inconsistent with the expectations elicited by the combination of context and discourse connective. Moreover, our experiments reveal that the facilitation of downstream material based on earlier connectives comes at the cost of reversing original expectations, as evidenced by a P600 effect on the concessive relative to the causal connective

A PDTB-Styled End-to-End Discourse Parser

We have developed a full discourse parser in the Penn Discourse Treebank (PDTB) style. Our trained parser first identifies all discourse and non-discourse relations, locates and labels their arguments, and then classifies their relation types. When appropriate, the attribution spans to these relations are also determined. We present a comprehensive evaluation from both component-wise and error-cascading perspectives.Comment: 15 pages, 5 figures, 7 table