Chuanqi Tan


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Predicting Clinical Trial Results by Implicit Evidence Integration
Qiao Jin | Chuanqi Tan | Mosha Chen | Xiaozhong Liu | Songfang Huang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Clinical trials provide essential guidance for practicing Evidence-Based Medicine, though often accompanying with unendurable costs and risks. To optimize the design of clinical trials, we introduce a novel Clinical Trial Result Prediction (CTRP) task. In the CTRP framework, a model takes a PICO-formatted clinical trial proposal with its background as input and predicts the result, i.e. how the Intervention group compares with the Comparison group in terms of the measured Outcome in the studied Population. While structured clinical evidence is prohibitively expensive for manual collection, we exploit large-scale unstructured sentences from medical literature that implicitly contain PICOs and results as evidence. Specifically, we pre-train a model to predict the disentangled results from such implicit evidence and fine-tune the model with limited data on the downstream datasets. Experiments on the benchmark Evidence Integration dataset show that the proposed model outperforms the baselines by large margins, e.g., with a 10.7% relative gain over BioBERT in macro-F1. Moreover, the performance improvement is also validated on another dataset composed of clinical trials related to COVID-19.


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SuperAgent: A Customer Service Chatbot for E-commerce Websites
Lei Cui | Shaohan Huang | Furu Wei | Chuanqi Tan | Chaoqun Duan | Ming Zhou
Proceedings of ACL 2017, System Demonstrations

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Entity Linking for Queries by Searching Wikipedia Sentences
Chuanqi Tan | Furu Wei | Pengjie Ren | Weifeng Lv | Ming Zhou
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We present a simple yet effective approach for linking entities in queries. The key idea is to search sentences similar to a query from Wikipedia articles and directly use the human-annotated entities in the similar sentences as candidate entities for the query. Then, we employ a rich set of features, such as link-probability, context-matching, word embeddings, and relatedness among candidate entities as well as their related entities, to rank the candidates under a regression based framework. The advantages of our approach lie in two aspects, which contribute to the ranking process and final linking result. First, it can greatly reduce the number of candidate entities by filtering out irrelevant entities with the words in the query. Second, we can obtain the query sensitive prior probability in addition to the static link-probability derived from all Wikipedia articles. We conduct experiments on two benchmark datasets on entity linking for queries, namely the ERD14 dataset and the GERDAQ dataset. Experimental results show that our method outperforms state-of-the-art systems and yields 75.0% in F1 on the ERD14 dataset and 56.9% on the GERDAQ dataset.


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Solving and Generating Chinese Character Riddles
Chuanqi Tan | Furu Wei | Li Dong | Weifeng Lv | Ming Zhou
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing


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Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification
Li Dong | Furu Wei | Chuanqi Tan | Duyu Tang | Ming Zhou | Ke Xu
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)