WantWords:一个开源的在线反向词典

<p align="center"> <a href="https://wantwords.thunlp.org/"> <img src="resources/wantwords_logo.png" width = "300" alt="WantWords Logo" align=center /> </a> </p> <h3 align="center">An open-source online reverse dictionary </h3> ## What Is a Reverse Dictionary? Opposite to a regular (forward) dictionary that provides definitions for query words, a reverse dictionary returns words semantically matching the query descriptions. <img src="resources/rd_example.png" alt="rd_example" style="zoom:50%;" /> ## What Can a Reverse Dictionary Do? * Solve the *tip-of-the-tongue problem*, the phenomenon of failing to retrieve a word from memory * Help new language learners * Help word selection (or word dictionary) anomia patients, people who can recognize and describe an object but fail to name it due to neurological disorder ## Our System ### Workflow <img src="resources/workflow.png" alt="workflow" style="zoom:20%;" /> ### Core Model The core model of WantWords is based on our proposed **Multi-channel Reverse Dictionary Model** [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/5365/5221)] [[code](https://github.com/thunlp/MultiRD)], as illustrate in the following figure. <img src="resources/MRD_model.png" alt="model" style="zoom:20%;" /> ### Pre-trained Models and Data [Download](https://cloud.tsinghua.edu.cn/f/db14bbfb78e0452bb2f1/) and decompress data and pretrained models to BASE_PATH/website_RD/. Decompress `data.rar` under BASE_PATH/website_RD/. ### Key Requirements * Django==2.2.5 * django-cors-headers==3.5.0 * numpy==1.17.2 * pytorch-transformers==1.2.0 * requests==2.22.0 * scikit-learn==0.22.1 * scipy==1.4.1 * thulac==0.2.0 * torch==1.2.0 * urllib3==1.25.6 * uWSGI==2.0.18 * uwsgitop==0.11 ## Cite If the code or data help you, please cite the following two papers. ``` @inproceedings{qi2020wantwords, title={WantWords: An Open-source Online Reverse Dictionary System}, author={Qi, Fanchao and Zhang, Lei and Yang, Yanhui and Liu, Zhiyuan and Sun, Maosong}, booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations}, pages={175--181}, year={2020} } @inproceedings{zhang2020multi, title={Multi-channel reverse dictionary model}, author={Zhang, Lei and Qi, Fanchao and Liu, Zhiyuan and Wang, Yasheng and Liu, Qun and Sun, Maosong}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, pages={312--319}, year={2020} } ```