Name
Abstract | ||
|---|---|---|
We provide a method for automatically detecting change in language across time through a chronologically trained neural language model. We train the model on the Google Books Ngram corpus to obtain word vector representations specific to each year, and identify words that have changed significantly from 1900 to 2009. The model identifies words such as "cell" and "gay" as having changed during that time period. The model simultaneously identifies the specific years during which such words underwent change. |
Year | Venue | Field |
|---|---|---|
2014 | meeting of the association for computational linguistics | Computer science,Speech recognition,Artificial intelligence,Natural language processing,Language model |
DocType | Volume | ISSN |
Journal | abs/1405.3515 | Proceedings of the ACL 2014 Workshop on Language Technologies and
Computational Social Science. June, 2014. 61--65 |
Citations | PageRank | References |
37 | 2.02 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoon Kim | 1 | 1533 | 57.57 |
| Yi-I. Chiu | 2 | 44 | 3.84 |
| Kentaro Hanaki | 3 | 37 | 2.02 |
| Darshan Hegde | 4 | 37 | 2.02 |
| Slav Petrov | 5 | 2405 | 107.56 |