By Henk Zeevat, Hans-Christian Schmitz
The contributions during this quantity specialise in the Bayesian interpretation of common languages, that's normal in parts of synthetic intelligence, cognitive technological know-how, and computational linguistics. this is often the 1st quantity to soak up themes in Bayesian usual Language Interpretation and make proposals according to details idea, likelihood idea, and similar fields. The methodologies provided right here expand to the objective semantic and pragmatic analyses of computational usual language interpretation. Bayesian techniques to usual language semantics and pragmatics are according to tools from sign processing and the causal Bayesian types pioneered by means of specially Pearl. In sign processing, the Bayesian procedure reveals the main possible interpretation by way of discovering the one who maximizes the made from the previous likelihood and the chance of the translation. It therefore stresses the significance of a creation version for interpretation as in Grice’s contributions to pragmatics or in interpretation through abduction.
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Additional resources for Bayesian Natural Language Semantics and Pragmatics
4 Extensions of the Basic Model In the basic model we only considered the case in which the assumed speaker of the test sentence believes that the true number N can lie anywhere in an open interval (n, m). When he produces an utterance with ‘more than’, then he always chooses n as the lower bound. This was encoded in Eq. (4). In this section, we consider other strategies which may lead to utterances containing ‘more than n’. The reasoning is admittedly even more speculative than in the previous sections.
1 Example item from Cummins et al. (2012, p. 146) 30 A. Benz Low High n = 100 100 (40) 150 (24) 101 (28) 125 (12) 120 (8) 200 (7) 1000 (5) n = 110 110 (46) 120 (28) 111 (31) 150 (24) 100 (9) 200 (7) 119 (6) 130 (5) 115 (5) Low n = 93 93 (32) 94 (29) 90 (14) High 100 (34) 95 (14) 99 (7) 150 (6) 125 (5) Fig. 2 Experimental results from Cummins et al. (2012) Cummins et al. considered the modifiers ‘more than’ and ‘at least’. We are only concerned with the cases in which the numerical expression of the test item was ‘more than n’.
The empirical effects of the inference shows that animacy is in fact a cue for being the subject of the sentence. It does not follow however that any stochastic regularity is a cue or that the inference as formulated above is the correct one. Should it not be merely concluded from animacy that the referent is relatively agentive in terms of Dowty (1990) proto-agent properties? It is basically unclear in terms of which properties the brain does its grammar and learns the cues that it uses. Grammatical, lexical and typological generalisations are only imperfect windows on these matters, but they seem to be the only windows that there are.
Bayesian Natural Language Semantics and Pragmatics by Henk Zeevat, Hans-Christian Schmitz