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The interpretation of conditionals is a long but ongoing debate in linguistics (cf. von Fintel 2007, 2011, 2012). In this paper, we focus on the meaning contribution of conditional connectives (CCs). According to Kratzer (1986, 1991), the English CC if has no semantics on its own and if-clauses are used to restrict modal or generic frequency operators. The restrictor analysis of conditionals and CCs has inspired many insightful follow-up studies through which it becomes clear that the interpretation of conditional sentences is subject to a process of semantic and pragmatic modulation. However, the role of CCs in the modulation process remains far from clear in the current literature, although CCs can influence the interpretation of conditional sentences in various ways. In this paper, we will argue with distributional observations and experimental evidence that Mandarin CCs can express different degrees of speaker commitment (Giannakidou and Mari 2015) towards the antecedent proposition, as non-at-issue meaning.

Numerical concepts are an integral part of everyday conversation and communication. Expressions relating to numbers in natural language can have precise or imprecise interpretations. While the precise interpretation most prominently appears in mathematical contexts, the imprecise interpretation seems to arise when numbers (as quantities) are applied to real world contexts (e.g., the rope is 50 m long). Earlier literature shows that the (im)precise interpretation can depend on different factors, e.g., the kind of approximator a numeral appears with (precise vs. imprecise, e.g., exactly vs. roughly) or the kind of numeral itself (round vs. non-round, e.g., 50 vs. 47). We report on a corpus-linguistic study and a rating experiment of English numerical expressions. The results confirm the effects of both factors and additionally an effect of the kind of unit (discrete vs. continuous, e.g., people vs. meters). This shows the contextual variability in the interpretation of numerical concepts in natural language.

It is impossible to talk about human cognition without talking about concepts—there simply is no human cognition without concepts. Concepts form an abstraction of reality that is central to the functioning of the human mind. Conceptual knowledge (of e.g., APPLE, LOVE and BEFORE) is crucial for us to categorize, understand, and reason about the world. Only equipped with concepts and words for them can we successfully communicate and carry out actions. But what exactly are concepts? How are concepts acquired? How does the human mind use concepts? In this introduction chapter, we elaborate on these questions (i.e., representation, learning, application), and provide summaries of the seven individual chapters in the volume, which from different scientific disciplines relate to one or the other of the questions.

In this paper, I argue that pragmatics plays a role in the (anti-)licensing of polarity items in addition to semantic notions such as downward monotonicity and anti-additivity. In the case of positive polarity items (PPIs), I argue that they can co-occur with the anti-additive quantifier no N if intonation or enriched context makes it a contrastive negation or denial. The anti-licensing fails due to a positive implicature (PI) that performs pragmatic licensing and it is to this PI that PPIs contribute their meaning. As for negative polarity items (NPIs), I focus on the case of only that is not straightforwardly downward monotonic but licenses NPIs. Following Horn (2002), I assume that only is semantically conjunctive and that it licenses NPIs by its exclusive entailment that is negative. In addition to Horn’s arguments, I provide further arguments with domain-widening NPIs such as any, ever and minimizers that it is not to the prejacent but to the exclusive entailment that they contribute their domain widening (Kadmon and Landman 1993) function. In other words, the problem of only for NPI theories does not lie in the notion of downward monotonicity but in the compact packaging of two propositions with different monotonicity properties in one single sentence.