Using Word Embedding to Evaluate the Coherence of Topics from Twitter Data.
Anjie Fang|Craig Macdonald|Iadh Ounis|Philip Habel
| Anthology ID: | DBLP:conf/sigir/FangMOH16a |
|---|---|
| Volume: | Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, July 17-21, 2016 |
| Year: | 2016 |
| Venue: | Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) |
| Publisher: | ACM |
| Pages: | 1057-1060 |
| URL: | https://doi.org/10.1145/2911451.2914729 |
| DOI: | https://doi.org/10.1145/2911451.2914729 |
| DBLP: | conf/sigir/FangMOH16a |
| BibTeX: |
@inproceedings{fang-2016-using,
author = {Anjie Fang and
Craig Macdonald and
Iadh Ounis and
Philip Habel},
editor = {Raffaele Perego and
Fabrizio Sebastiani and
Javed A. Aslam and
Ian Ruthven and
Justin Zobel},
title = {{Using Word Embedding to Evaluate the Coherence of Topics from Twitter Data}},
booktitle = {{Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, July 17-21, 2016}},
pages = {1057--1060},
publisher = {ACM},
year = {2016},
url = {https://doi.org/10.1145/2911451.2914729},
doi = {https://doi.org/10.1145/2911451.2914729}
}