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Tensor factorization methods for lexical acquisition and the modeling of semantic compositionality| old_uid | 14979 |
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| title | Tensor factorization methods for lexical acquisition and the modeling of semantic compositionality |
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| start_date | 2015/01/23 |
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| schedule | 11h-12h30 |
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| online | no |
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| location_info | salle 255 |
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| summary | In this talk, we will look at a number of tensor factorization methods
for the modeling of language data. First, we present a method for the
joint unsupervised aquisition of verb subcategorization frame (SCF)
and selectional preference (SP) information. Treating SCF and SP
induction as a multi-way co-occurrence problem, we use multi-way
tensor factorization to cluster frequent verbs from a large corpus
according to their syntactic and semantic behaviour. The method is
able to predict whether a syntactic argument is likely to occur with a
verb lemma (SCF) as well as which lexical items are likely to occur in
the argument slot (SP). Secondly, we present a method for the
computation of semantic compositionality within a distributional
framework. We use our method to model the composition of subject verb
object triples. The key idea is that compositionality is modeled as a
multi-way interaction between latent factors, which are automatically
constructed from corpus data. The method consists of two steps. First,
we compute a latent factor model for nouns from standard co-occurrence
data. Next, the latent factors are used to induce a latent model of
three-way subject verb object interactions. By treating language data
as multi-way co-occurrence frequencies, both methods are able to
properly model the tasks at hand in an entirely unsupervised way. |
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| responsibles | Candito |
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