We study the problem of learning with label proportions in which the training data is
provided in groups and only the proportion of each class in each group is known. We …

We propose a general information-theoretic approach to semi-supervised metric learning
called SERAPH (SEmi-supervised metRic leArning Paradigm with Hypersparsity) that does …

We present a novel method for the crosslingual transfer of dependency parsers. Our goal is
to induce a dependency parser in a target language of interest without any direct …

This study systematically reviews existing approaches to unsupervised grammar induction in
terms of their theoretical underpinnings, practical implementations and evaluation. Our …

We show how punctuation can be used to improve unsupervised dependency parsing. Our
linguistic analysis confirms the strong connection between English punctuation and phrase …

Most of the unsupervised dependency parsers are based on first-order probabilistic
generative models that only consider local parent-child information. Inspired by second …

Many statistical learning problems in NLP call for local model search methods. But accuracy
tends to suffer with current techniques, which often explore either too narrowly or too …

There has been recent interest in applying cognitively-or empirically-motivated bounds on
recursion depth to limit the search space of grammar induction models (Ponvert et al., 2011; …

An HDP Model for Inducing Combinatory Categorial Grammars Page 1 Transactions of the
Association for Computational Linguistics, 1 (2013) 75–88. Action Editor: Sharon Goldwater …

Learning a classifier from groups of unlabeled data, only knowing, for each group, the
proportions of data with particular labels, is an important branch of classification tasks that …