Elements of Formal Semantics has already been reviewed twice :42, 2016; Erlewine in Comput Linguist 42:837–839, 2017). As well, the website for the work is accompanied by evaluative quotes by noted scholars. All are very positive concerning its clarity and its utility as an introduction to formal semantics for natural language. As I agree with these evaluations my interest in reiterating them in slightly different words is limited. So my reviews of the content chapters will be accompanied by a Reflections (...) section consisting of my own reflections on the foundations of model theoretic semantics for natural language as laid out in EFS. The issues I address—alternate ways of accomplishing the tasks Winter treats—should not be included in an introductory work but they may be helpful for those who teach classes for which EFS is an appropriate text. They might also help with queries about the content of the text by those using it. I note that a mark of a clear text is that it allows the reader to reflect on its content not its presentation. (shrink)

In this paper, I show that the availability of what some authors have called the weak reading and the strong reading of donkey sentences with relative clauses is systematically related to monotonicity properties of the determiner. The correlation is different from what has been observed in the literature in that it concerns not only right monotonicity, but also left monotonicity (persistence/antipersistence). I claim that the reading selected by a donkey sentence with a double monotone determiner is in fact the one (...) that validates inference based on the left monotonicity of the determiner. This accounts for the lack of strong reading in donkey sentences with MON determiners, which have been neglected in the literature. I consider the relevance of other natural forms of inference as well, but also suggest how monotonicity inference might play a central role in the actual process of interpretation. The formal theory is couched in dynamic predicate logic with generalized quantifiers. (shrink)