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 TERM NATURAL language refers to any system of symbolic communication (spoken,
signed, or written) that has evolved naturally in humans without intentional human planning …

We propose a novel neural network model for joint part-of-speech (POS) tagging and
dependency parsing. Our model extends the well-known BIST graph-based dependency …

Traditional syntax models typically leverage part-of-speech (POS) information by
constructing features from hand-tuned templates. We demonstrate that a better approach is …

This paper presents new state-of-the-art models for three tasks, part-of-speech tagging,
syntactic parsing, and semantic parsing, using the cutting-edge contextualized embedding …

Graph embedding is an important representational technique that aims to maintain the
structure of a graph while learning low-dimensional representations of its vertices. Semantic …

We introduce the structured projection of intermediate gradients optimization technique
(SPIGOT), a new method for backpropagating through neural networks that include hard …

The representation of words in the vector space generated using the Word2Vec model does
not capture the syntactic similarity between sentences, but only the semantic similarity by …

Chinese word segmentation and dependency parsing are two fundamental tasks for
Chinese natural language processing. The dependency parsing is defined at the word-level …

Abstract Space-delimited words in Turkish and Hebrew text can be further segmented into
meaningful units, but syntactic and semantic context is necessary to predict segmentation. At …