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|Full Title:||Workshop on Bayesian Natural Language Semantics and Pragmatics|
|Start Date:||05-Aug-2013 - 09-Aug-2013|
|Meeting Email:||click here to access email|
|Meeting Description:||Workshop on Bayesian Natural Language Semantics and Pragmatics
Organised as part of the European Summer School on Logic, Language and Information (ESSLLI 2013, http://esslli2013.de/) August 5-9, Heinrich Heine University, Düsseldorf/Germany
Bayesian interpretation is a standard technique in signal interpretation in which the most probable message M conveyed by a signal S is found by using two models, namely the prior probability of the message M and the production probability of the signal S, that is, the probability of the signal given the message. Since by Bayes’ theorem argmaxM p(M|S) = argmaxM p(M)p(S|M), the two models suffice for detecting the most probable message given the signal. Bayesian NL interpretation is just the same: the signal is an utterance (of a word, sentence, turn or text), and the messages, that is the interpretation hypotheses range over the possible intentions of the speaker, which according to Grice the hearer must recognise in a successful communication. Bayesian methods include Bayesian nets, Bayesian belief revision and information states that are represented as probability distributions, among other methods.
The workshop wants to collect emerging work in Bayesian interpretation as well as work using Bayesian methods in natural language (NL) interpretation and bring together the various approaches so as to contribute to a more integrated research programme in this new area.
Jacques Jayez, ENS Lyon & CNRS L2C2
Stefan Kaufmann, University of Connecticut & Northwestern University
Daniel Lassiter, Stanford University
The workshop receives funding from the German Society for Computational Linguistics & Language Technology (GSCL).
|Linguistic Subfield:||Cognitive Science; Philosophy of Language; Pragmatics; Semantics|
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