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<p align="center"> <a href="https://wantwords.thunlp.org/"> <img src="resources/wantwords_logo.svg" width = "300" alt="WantWords Logo" /> </a> </p> <h3 align="center">An Open-source Online Reverse Dictionary [<a href="https://wantwords.net/">link</a>] </h3>News
The WantWords MiniProgram has been launched. Welcome to scan the following QR code to try it!
<div align=center> <img src="resources/miniprogram.jpg" width = "300" alt="MiniProgram QR code"/> </div>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.
<div align=center> <img src="resources/rd_example.png" alt="rd_example" width = "600"/> </div>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
<div align=center> <img src="resources/workflow.png" alt="workflow" width = "500" /> </div>Core Model
The core model of WantWords is based on our proposed Multi-channel Reverse Dictionary Model [paper] [code], as illustrate in the following figure.
<div align=center> <img src="resources/MRD_model.png" alt="model" width = "500" /> </div>Pre-trained Models and Data
You can download and decompress the pre-trained models and data to BASE_PATH/website_RD/ to reimplement the system.
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}
}