Learning to Explain Non-Standard English Words and Phrases
arXiv:1709.09254
Abstract
We describe a data-driven approach for automatically explaining new, non-standard English expressions in a given sentence, building on a large dataset that includes 15 years of crowdsourced examples from UrbanDictionary.com. Unlike prior studies that focus on matching keywords from a slang dictionary, we investigate the possibility of learning a neural sequence-to-sequence model that generates explanations of unseen non-standard English expressions given context. We propose a dual encoder approach---a word-level encoder learns the representation of context, and a second character-level encoder to learn the hidden representation of the target non-standard expression. Our model can produce reasonable definitions of new non-standard English expressions given their context with certain confidence.
arXiv commentIJCNLP 2017
References in corpus (1)
Cited by in corpus (5)
- A Computational Framework for Slang Generation
- Multi-sense Definition Modeling using Word Sense Decompositions
- Emo, Love, and God: Making Sense of Urban Dictionary, a Crowd-Sourced Online Dictionary
- Learning to Describe Phrases with Local and Global Contexts
- How to Evaluate Word Representations of Informal Domain?