| ISIKUN at the FinCausal 2020: Linguistically informed Machine-learning Approach for Causality Identification in Financial Documents | Dec 1, 2020 | Binary ClassificationTask 2 | CodeCode Available | 0 |
| Fraunhofer IAIS at FinCausal 2020, Tasks 1 & 2: Using Ensemble Methods and Sequence Tagging to Detect Causality in Financial Documents | Dec 1, 2020 | Task 2 | —Unverified | 0 |
| The Financial Document Causality Detection Shared Task (FinCausal 2020) | Dec 1, 2020 | Binary ClassificationRelation Extraction | —Unverified | 0 |
| LITL at SMM4H: An Old-school Feature-based Classifier for Identifying Adverse Effects in Tweets | Dec 1, 2020 | regressionTask 2 | —Unverified | 0 |
| HITSZ-ICRC: A Report for SMM4H Shared Task 2020-Automatic Classification of Medications and Adverse Effect in Tweets | Dec 1, 2020 | ClassificationTask 2 | —Unverified | 0 |
| Identifying Medication Abuse and Adverse Effects from Tweets: University of Michigan at #SMM4H 2020 | Dec 1, 2020 | Task 2 | —Unverified | 0 |
| Sentence Classification with Imbalanced Data for Health Applications | Dec 1, 2020 | ClassificationSentence | —Unverified | 0 |
| How Far Can We Go with Just Out-of-the-box BERT Models? | Dec 1, 2020 | PharmacovigilanceTask 2 | —Unverified | 0 |
| Approaching SMM4H 2020 with Ensembles of BERT Flavours | Dec 1, 2020 | Task 2 | —Unverified | 0 |
| Adverse Drug Reaction Detection in Twitter Using RoBERTa and Rules | Dec 1, 2020 | Task 2Transfer Learning | —Unverified | 0 |
