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By T. Ogihara

Stressful, Attitudes, and Scope is a model-theoretic inquiry into the semantics of demanding in normal language. The e-book provides the view that the semantic contribution of stressful is made in terms of structurally better expressions (the `relative demanding theory') and argues opposed to the view that tenses are all indexicals. this concept is officially encoded as a de se research of attitudes, initially proposed through Lewis, coupled with a sequence-of-tense rule posited for English. An auxiliary notion is made to account for a few unparalleled situations (e.g. so-called double-access sentences), which invokes de re attitudes approximately temporal entities (states or intervals). due to the fact the proposed account assumes that the translation of annoying is structure-dependent, it additionally effectively predicts scope interactions among tenses and NPs. annoying, Attitudes, and Scope is meant for students and graduate scholars in formal semantics, syntax-semantics interface, philosophy of language and eastern linguistics.

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We want to finish this section noticing one relationship that we have found between the two mentioned perspectives tackling the semantic interoperability issue in the introduction. In [1] the authors of the cream system, that consider the data integration perspective, define 12 different message types for a semantic mediator communication protocol, namely ask-all, ask-if, deliver, detect, generate, locate, reconcile, reply-all, reply-if, report, resolve-if and tell. The protocol contains message types for encapsulating all basic communication events they might need.

The same-as relation denotes that c and c are equivalent, while the kind-of and part-of relations denote that c and c are related by a specialization relation and a composition relation, respectively. Finally, the associates relation denotes that c and c are related by a generic positive semantic association. In Figure 1, examples of how OWL constructs are mapped on such semantic relations are given. Like linguistic features, also contextual features are weighted in H-Match. In particular, we associate a weight Wsp to strong properties, and a weight Wwp to weak properties, with Wsp ≥ Wwp to capture the different importance each kind of property has in characterizing the concept.

The term affinity function A(t, t ) → [0, 1] evaluates the affinity between two terms t and t based on the thesaurus. The term affinity function is borrowed from Artemis and it is reported here for the sake of clarity. A(t, t ) of two terms t and t is equal to the value of the highest-strength path of terminological relationships between them in T h if at least one path exists, and is zero otherwise. k {Wt→ni t } if k ≥ 1 0 otherwise (1) where: k is the number of paths between t and t in T h; t →ni t denotes the ith path of length n ≥ 1; Wt→ni t = W1tr · W2tr · .

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