We study a formalization of the grammar induction problem that models sentences as being
generated by a compound probabilistic context-free grammar. In contrast to traditional …

We propose a method for formulating CCG as a recursive composition in a continuous
vector space. Recent CCG supertagging and parsing models generally demonstrate high …

Probabilistic context-free grammars (PCFGs) with neural parameterization have been shown
to be effective in unsupervised phrase-structure grammar induction. However, due to the …

In this article, we seek to automatically identify Hungarian patients suffering from mild
cognitive impairment (MCI) or mild Alzheimer disease (mAD) based on their speech …

Probabilistic context-free grammars (PCFGs) and dynamic Bayesian networks (DBNs) are
widely used sequence models with complementary strengths and limitations. While PCFGs …

We propose a ParametRIc MAnifold Learning (PRIMAL) algorithm for Gaussian mixtures
models (GMM), assuming that GMMs lie on or near to a manifold of probability distributions …

Neural models have been investigated for sentiment classification over constituent trees.
They learn phrase composition automatically by encoding tree structures but do not explicitly …

In natural language processing, the syntax of a sentence refers to the words used in the
sentence, their grammatical role, and their order. Semantics concerns the concepts …

We show that a previously proposed algorithm for the N-best trees problem can be made
more efficient by changing how it arranges and explores the search space. Given an integer …

Structured representations build upon symbolic abstraction (eg, words in natural language
and visual concepts in natural images), offer a principled way of encoding our perceptions …