Deep Neural Models for Medical Concept Normalization in User-Generated Texts

<p>PsyTar&nbsp;folds used for experiments in the paper &quot;Deep Neural Models for Medical Concept Normalization in User-Generated Texts&quot;&nbsp;&nbsp;to be published at&nbsp;ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Student Research Workshop.&nbsp;</p> <p>All other datasets used in the paper&nbsp;can be found in the following places:</p> <p>Cadec&nbsp;random:&nbsp;https://zenodo.org/record/55013#.XPE1MC1eN24<br> Cadec custom:&nbsp;https://yadi.sk/d/GZoWm1wBxzyW_w</p> <p>SMM4H dataset: in the paper &quot;Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H) - 2017 shared task&quot;<br> <br> Bibtex:</p> <p>@inproceedings{miftahutdinov2019,<br> &nbsp; &nbsp; title = &quot;Deep Neural Models for Medical Concept Normalization in User-Generated Texts&quot;,<br> &nbsp; &nbsp; author = &quot;Miftahutdinov, Zulfat and Tutubalina, Elena&quot;,<br> &nbsp; &nbsp; booktitle = &quot;Proceedings of {ACL} 2019, Student Research Workshop&quot;,<br> &nbsp; &nbsp; month = jul,<br> &nbsp; &nbsp; year = &quot;2019&quot;,<br> &nbsp; &nbsp; address = &quot;Florence, Italy&quot;,<br> &nbsp; &nbsp; publisher = &quot;Association for Computational Linguistics&quot;,<br> }</p>