A Hierarchical Deep Learning Natural Language Parser for Fashion
arXiv:1806.09511
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.
arXiv commentIn Proceedings of KDD 2018 (KDD Workshop on AI for Fashion)
References in corpus (8)
- Efficient Estimation of Word Representations in Vector Space
- Natural Language Processing (almost) from Scratch
- Enhanced LSTM for Natural Language Inference
- Transition-Based Dependency Parsing with Stack Long Short-Term Memory
- TnT - A Statistical Part-of-Speech Tagger
- End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
- How Transferable are Neural Networks in NLP Applications?
- A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks