## News
The WantWords MiniProgram has been launched. Welcome to scan the following QR code to try it!
## 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.
## 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
### 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.
### Pre-trained Models and Data
You can [download](https://cloud.tsinghua.edu.cn/d/811dcb428ed24480bc60/) 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.
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