The understanding and acquisition of a language in a real-world environment is an
important task for future robotics services. Natural language processing and cognitive …

Natural language parsing has typically been done with small sets of discrete categories
such as NP and VP, but this representation does not capture the full syntactic nor semantic …

We design experiments to study $\textit {how} $ generative language models, like GPT, learn
context-free grammars (CFGs)--diverse language systems with a tree-like structure capturing …

The Handbook of Natural Language Processing, Second Edition presents practical tools
and techniques for implementing natural language processing in computer systems. Along …

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 present an automatic approach to tree annotation in which basic nonterminal symbols
are alternately split and merged to maximize the likelihood of a training treebank. Starting …

Recurrent neural network grammars (RNNG) are generative models of language which
jointly model syntax and surface structure by incrementally generating a syntax tree and …

Dependency parsing has been a topic of active research in natural language processing
during the last several years. The CoNLL-2006 shared task (Buchholz and Marsi, 2006) …

We present several improvements to unlexicalized parsing with hierarchically state-split
PCFGs. First, we present a novel coarse-to-fine method in which a grammar's own …

We present a simple and effective semisupervised method for training dependency parsers.
We focus on the problem of lexical representation, introducing features that incorporate word …