32 citations · 187 across the 35 of their papers we have counts for
5 papers · 1 filter
Capturing Long-range Contextual Dependencies with Memory-enhanced Conditional Random Fields
Fei Liu, Timothy Baldwin, Trevor Cohn
Despite successful applications across a broad range of NLP tasks, conditional random fields ("CRFs"), in particular the linear-chain variant, are only able to model local features…
Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks
Afshin Rahimi, Timothy Baldwin, Trevor Cohn
We propose a method for embedding two-dimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presen…
An Automatic Approach for Document-level Topic Model Evaluation
Shraey Bhatia, Jey Han Lau, Timothy Baldwin
Topic models jointly learn topics and document-level topic distribution. Extrinsic evaluation of topic models tends to focus exclusively on topic-level evaluation, e.g. by assessin…
Topically Driven Neural Language Model
Jey Han Lau, Timothy Baldwin, Trevor Cohn
Language models are typically applied at the sentence level, without access to the broader document context. We present a neural language model that incorporates document context i…
Context-Aware Prediction of Derivational Word-forms
Ekaterina Vylomova, Ryan Cotterell, Timothy Baldwin +1
Derivational morphology is a fundamental and complex characteristic of language. In this paper we propose the new task of predicting the derivational form of a given base-form lemm…