Name
Abstract | ||
|---|---|---|
Given a set of images with related captions, our goal is to show how visual features can improve the accuracy of unsupervised word sense disambiguation when the textual context is very small, as this sort of data is common in news and social media. We extend previous work in unsupervised text-only disambiguation with methods that integrate text and images. We construct a corpus by using Amazon Mechanical Turk to caption sense-tagged images gathered from ImageNet. Using a Yarowsky-inspired algorithm, we show that gains can be made over text-only disambiguation, as well as multimodal approaches such as Latent Dirichlet Allocation. |
Year | Venue | Keywords |
|---|---|---|
2012 | *SEM@NAACL-HLT | yarowsky-inspired algorithm,text-only disambiguation,sense-tagged image,unsupervised disambiguation,social media,latent dirichlet allocation,unsupervised word sense disambiguation,previous work,amazon mechanical turk,image caption,unsupervised text-only disambiguation,related caption |
Field | DocType | Citations |
Latent Dirichlet allocation,Social media,Information retrieval,Computer science,sort,Natural language processing,Artificial intelligence,Word-sense disambiguation | Conference | 3 |
PageRank | References | Authors |
0.40 | 14 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wesley May | 1 | 3 | 0.40 |
| Sanja Fidler | 2 | 2087 | 116.71 |
| Afsaneh Fazly | 3 | 213 | 26.99 |
| Sven J. Dickinson | 4 | 2836 | 185.12 |
| Suzanne Stevenson | 5 | 566 | 64.31 |