Name
Abstract | ||
|---|---|---|
Compared with carefully edited prose, the language of social media is informal in the extreme. The application of NLP techniques in this context may require a better understanding of word usage within social media. In this paper, we compute a word embedding for a corpus of tweets, comparing it to a word embedding for Wikipedia. After learning a transformation of one vector space to the other, and adjusting similarity values according to term frequency, we identify words whose usage differs greatly between the two corpora. For any given word, the set of words closest to it in a particular embedding provides a characterization for that word's usage within the corresponding corpora. |
Year | Venue | DocType |
|---|---|---|
2015 | PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL) AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (IJCNLP), VOL 2 | Conference |
Volume | Citations | PageRank |
P15-2 | 6 | 0.47 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luchen Tan | 1 | 55 | 9.04 |
| Haotian Zhang | 2 | 294 | 23.41 |
| Charles L.A. Clarke | 3 | 3289 | 286.78 |
| Mark D. Smucker | 4 | 948 | 60.04 |