32 citations · 300 across the 54 of their papers we have counts for
4 papers · 1 filter
Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning
Ekaterina Vylomova, Laura Rimell, Trevor Cohn +1
Recent work on word embeddings has shown that simple vector subtraction over pre-trained embeddings is surprisingly effective at capturing different lexical relations, despite lack…
Exploiting Text and Network Context for Geolocation of Social Media Users
Afshin Rahimi, Duy Vu, Trevor Cohn +1
Research on automatically geolocating social media users has conventionally been based on the text content of posts from a given user or the social network of the user, with very l…
Twitter User Geolocation Using a Unified Text and Network Prediction Model
Afshin Rahimi, Trevor Cohn, Timothy Baldwin
We propose a label propagation approach to geolocation prediction based on Modified Adsorption, with two enhancements:(1) the removal of "celebrity" nodes to increase location homo…
Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representation on Sequence Labelling Tasks
Lizhen Qu, Gabriela Ferraro, Liyuan Zhou +3
Word embeddings -- distributed word representations that can be learned from unlabelled data -- have been shown to have high utility in many natural language processing application…