Seminaire: ALPAGE, vendredi 9 septembre (Marie-Catherine de Marneffe)

Thierry Hamon thierry.hamon at UNIV-PARIS13.FR
Sat Sep 3 17:26:30 UTC 2011

Date: Thu, 1 Sep 2011 17:08:54 +0200
From: "Benoit Crabbé" <benoit.crabbe at>
Message-Id: <850717F3-8DEC-4238-A293-E9CB99384ECF at>

*************** Séminaire Alpage *******************

Séminaire de l'école doctorale de Paris 7

Il s'agit du séminaire de recherche en linguistique informatique
organisé par l'équipe Alpage, Alpage est une équipe mixte Inria -- Paris
7 qui centre ses intérêts scientifiques autour de l'analyse syntaxique
automatique et du traitement du discours pour la langue française.


Le prochain séminaire se tiendra vendredi 9 septembre de 13.30 à 15.00
en salle 3E91 à l'UFRL, 175, rue du Chevaleret, 75013 Paris (3e étage)

Toute personne intéressée est la bienvenue.


Marie-Catherine de Marneffe (Stanford)

nous parlera de :

Monitoring distributional patterns in our input: probabilistic models
for production and comprehension.


In this talk I will propose two different probabilistic models that show
how our language production and comprehension heavily rely on keeping
track of patterns present in what we hear.

The first part of the talk concentrates on children's
production. Focusing on the dative alternation in English, I examine
whether children's choices are influenced by the same factors that
influence adults' choices. I also look at whether children are sensitive
to multiple factors simultaneously. Using mixed effects regression
models, I find parallels between child and adult speech, consistent with
recent acquisition research suggesting that there is a usage-based
continuity between child and adult grammars. The results demonstrate
that from early on children pay attention to complex distributional
patterns, and replicate the subtle patterns found in their input.

The second part of the talk targets adults' comprehension when dealing
with errorful input. I examine number agreement errors, which are
relatively common in production. I suggest that comprehension of such
input reflects an understanding of likely production errors:
comprehenders know the likelihood of different kinds of production
errors, and recover more easily from more probable errors. I use a
comprehension model which represents an optimal allocation of processing
resources under noisy input (Levy 2008). The results show that
language-specific estimates of these likelihoods result in a better fit
to comprehension data, and that such a model predicts behavior in two
domains: syntactically well-formed local coherences as reported by Tabor
et al. (2004) and syntactically ill-formed agreement errors as reported
by Pearlmutter et al. (1999).


Marie-Catherine de Marneffe is a final year PhD student in Linguistics
at Stanford University and a member of the Stanford NLP group. Her
dissertation examines how to automatically retrieve the meaning that
humans access when faced with a piece of text, going beyond the literal
meaning of utterances. Her work on computational pragmatics focuses on
detecting entailment and contradiction in texts, grounding meaning from
large unstructured databases, and assessing the information status of
events from a reader's perspective. She also developed the Stanford
dependencies, and worked on French parsing. She is interested in
language acquisition, studying how children acquire verb forms in

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