Concept-Based Embeddings for Natural Language Processing
Computation and Language
2018-07-17 v1
Abstract
In this work, we focus on effectively leveraging and integrating information from concept-level as well as word-level via projecting concepts and words into a lower dimensional space while retaining most critical semantics. In a broad context of opinion understanding system, we investigate the use of the fused embedding for several core NLP tasks: named entity detection and classification, automatic speech recognition reranking, and targeted sentiment analysis.
Cite
@article{arxiv.1807.05519,
title = {Concept-Based Embeddings for Natural Language Processing},
author = {Yukun Ma and Erik Cambria},
journal= {arXiv preprint arXiv:1807.05519},
year = {2018}
}