Mental-content verbs such as think, believe, imagine and hope seem to pose special problems for the young language learner. One possible explanation for these diYculties is that the concepts that these verbs express are hard to grasp and therefore their acquisition must await relevant conceptual development. According to a diVerent, perhaps complementary, proposal, a major contributor to the diYculty of these items lies with the informational requirements for identifying them from the contexts in which they appear. The experiments reported here (...) explore the implications of these proposals by investigating the contribution of observational and linguistic cues to the acquisition of mental predicate vocabulary. We Wrst demonstrate that particular observed situations can be helpful in prompting reference to mental contents, speciWcally, contexts that include a salient and/or unusual mental state such as a false belief. We then compare the potency of such observational support to the reliability of alternate or concomitant syntactic information (e.g., sentential complementation) in tasks where both children and adults are asked to hypothesize the meaning of novel verbs. The Wndings support the eYcacy of false belief situations for increasing the saliency of mental state descriptions, but also show that.. (shrink)

Cognitive Science, Volume 46, Issue 1, January 2022.

It is commonly assumed across the language sciences that some semantic participant information is lexically encoded in the representation of verbs and some is not. In this paper, we propose that semantic obligatoriness and verb class specificity are criteria which influence whether semantic information is lexically encoded. We present a comprehensive survey of the English verbal lexicon, a sentence continuation study, and an on-line sentence processing study which confirm that both factors play a role in the lexical encoding of participant (...) information. (shrink)

Selectional preferences have a long history in both generative and computational linguistics. However, since the publication of Resnik's dissertation in 1993, a new approach has surfaced in the computational linguistics community. This new line of research combines knowledge represented in a pre‐defined semantic class hierarchy with statistical tools including information theory, statistical modeling, and Bayesian inference. These tools are used to learn selectional preferences from examples in a corpus. Instead of simple sets of semantic classes, selectional preferences are viewed as (...) probability distributions over various entities. We survey research that extends Resnik's initial work, discuss the strengths and weaknesses of each approach, and show how they together form a cohesive line of research. (shrink)

The present paper argues that when thematic roles are restricted to judgments about causal properties of events, it falls short of accounting for cases where thematic roles reflect judgments about dispositional properties of objects. I develop my argument with a case study on a class of verbs that have been called ‘Emission Verbs’ and which are difficult to bring in line with the unaccusativity hypothesis put forward by Perlmutter. Reviewing two diametrically opposed accounts of Emission Verbs in the literature, I (...) show that the thematic-semantic relation between the events described by Emission Verbs and their single arguments cannot be characterized unambiguously in terms of causal properties of events but pertains to dispositional properties residing in the emitter argument. The paper develops a lexical-semantic analysis of Emission Verbs according to which the event described by an Emission Verb is the manifestation of the dispositional property of the emitter argument when appropriate external circumstances obtain. The paper concludes by outlining how the proposed dispositional analysis of Emission Verbs may inform the analysis of the causative alternation. (shrink)

Interdisciplinary investigations marry the methods and concerns of different fields. Computer science is the study of precise descriptions of finite processes; semantics is the study of meaning in language. Thus, computational semantics embraces any project that approaches the phenomenon of meaning by way of tasks that can be performed by following definite sets of mechanical instructions. So understood, computational semantics revels in applying semantics, by creating intelligent devices whose broader behavior fits the meanings of utterances, and not just their form. (...) IBM’s Watson (Ferrucci, Brown, Chu-Carroll, Fan, Gondek, Kalyanpur, Lally, Murdock, Nyberg, Prager, Schlaefer & Welty 2010) is a harbinger of the excitement and potential of this technology. (shrink)

Experimental research shows that human sentence processing uses information from different levels of linguistic analysis, for example, lexical and syntactic preferences as well as semantic plausibility. Existing computational models of human sentence processing, however, have focused primarily on lexico‐syntactic factors. Those models that do account for semantic plausibility effects lack a general model of human plausibility intuitions at the sentence level. Within a probabilistic framework, we propose a wide‐coverage model that both assigns thematic roles to verb–argument pairs and determines a (...) preferred interpretation by evaluating the plausibility of the resulting (verb, role, argument) triples. The model is trained on a corpus of role‐annotated language data. We also present a transparent integration of the semantic model with an incremental probabilistic parser. We demonstrate that both the semantic plausibility model and the combined syntax/semantics model predict judgment and reading time data from the experimental literature. (shrink)

How do children learn the meanings of propositional attitude verbs? We argue that children use information contained in both syntactic distribution and pragmatic function to zero in on the appropriate meanings. Specifically, we identify a potentially universal link between semantic subclasses of attitude verbs, their syntactic distribution and the kinds of indirect speech acts they can be used to perform. As a result, children can use the syntax as evidence about the meaning, which in turn constrains the kinds of pragmatic (...) enrichments they do and do not make in understanding these verbs in conversation. (shrink)

Fisher, Jin, and Scott push a central assumption of syntactic bootstrapping: that learners have a universal bias to map each noun in a sentence onto a participant role (i.e., argument of the verb). They propose two enrichments: First, that children use both semantic and syntactic information in representing nouns that accompany a verb; second, that children expect continuity across a discourse. They provide evidence for both learning mechanisms among young children, further spelling out the precise mechanisms underlying syntactic bootstrapping.

“Everyone knows that language is variable.” This is the bald sentence with which Sapir (1921:147) begins his chapter on language as an historical product. He goes on to emphasize how two speakers’ usage is bound to differ “in choice of words, in sentence structure, in the relative frequency with which particular forms or combinations of words are used”. I should add that much sociolinguistic and historical linguistic research has shown that the same speaker’s usage is also variable (Labov 1966, Kroch (...) 2001:722). However, the tradition of most syntacticians has been to ignore this thing that everyone knows. (shrink)