Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Brain activity in numerous perisylvian brain regions is modulated by the expectedness of
linguistic stimuli. We leverage recent advances in computational parsing models to test what …

Syntactic and semantic parsing has been investigated for decades, which is one primary
topic in the natural language processing community. This article aims for a brief survey on …

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

Parsing unrestricted text is useful for many language technology applications but requires
parsing methods that are both robust and efficient. MaltParser is a language-independent …

Parsing algorithms that process the input from left to right and construct a single derivation
have often been considered inadequate for natural language parsing because of the …

Shift-reduce dependency parsers give comparable accuracies to their chartbased
counterparts, yet the best shiftreduce constituent parsers still lag behind the state-of-the-art …

This article describes a number of log-linear parsing models for an automatically extracted
lexicalized grammar. The models are “full” parsing models in the sense that probabilities are …

Estimating probability distribution is one of the core issues in the NLP field. However, in both
deep learning (DL) and pre-DL eras, unlike the vast applications of linear-chain CRF in …

Dependency parsing is a form of syntactic parsing of natural language based on the
theoretical tradition of dependency grammar. It has recently gained widespread interest in …