Publications
Authors:
  • Audi Primadhanty , Xavier Carreras , Ariadna Quattoni
Citation:
ACL, Beijing, China, 26-31 July, 2015
Abstract:
Entity classification, like many other
important problems in NLP, involves
learning classifiers over sparse highdimensional
feature spaces that result
from the conjunction of elementary features
of the entity mention and its context.
In this paper we develop a low-rank regularization
framework for training maxentropy
models in such sparse conjunctive
feature spaces. Our approach handles conjunctive
feature spaces using matrices and
induces an implicit low-dimensional representation
via low-rank constraints. We
show that when learning entity classifiers
under minimal supervision, using a seed
set, our approach is more effective in controlling
model capacity than standard techniques
for linear classifiers.
Year:
2015
Report number:
2015/030
Attachments: