A Hierarchical Deep Learning Natural Language Parser for Fashion

TL;DR

This paper introduces a hierarchical deep learning natural language parser tailored for the fashion domain, capable of recognizing entities and understanding syntactic and morphological nuances, improving handling of textual ambiguities.

Contribution

It proposes a novel hierarchical architecture of specialist models for fashion NLP tasks, enhancing entity recognition and syntactic analysis over existing solutions.

Findings

Robust baseline established for fashion NLP parsing

Hierarchical models outperform flat architectures

Improved handling of textual ambiguities in fashion texts

Abstract

This work presents a hierarchical deep learning natural language parser for fashion. Our proposal intends not only to recognize fashion-domain entities but also to expose syntactic and morphologic insights. We leverage the usage of an architecture of specialist models, each one for a different task (from parsing to entity recognition). Such architecture renders a hierarchical model able to capture the nuances of the fashion language. The natural language parser is able to deal with textual ambiguities which are left unresolved by our currently existing solution. Our empirical results establish a robust baseline, which justifies the use of hierarchical architectures of deep learning models while opening new research avenues to explore.