MCPcopy
hub / github.com/thunlp/WantWords

github.com/thunlp/WantWords @main sqlite

repository ↗ · DeepWiki ↗
63 symbols 175 edges 10 files 0 documented · 0%
README

|En

WantWords Logo

An Open-source Online Reverse Dictionary [link]

News

The WantWords MiniProgram has been launched. Welcome to scan the following QR code to try it!

MiniProgram QR code

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.

rd_example

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

workflow

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.

model

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}
}

Core symbols most depended-on inside this repo

clearAlert
called by 19
static/js/home.js
showTable
called by 12
static/js/home.js
htmlDanger
called by 11
static/js/home.js
htmlInfo
called by 10
static/js/home.js
htmlInfo_E
called by 10
static/js/home.js
htmlWarning
called by 9
static/js/home.js
htmlWarning_E
called by 9
static/js/home.js
htmlDanger_E
called by 9
static/js/home.js

Shape

Function 57
Method 4
Class 2

Languages

TypeScript56%
Python44%

Modules by API surface

static/js/home.js34 symbols
website_RD/views.py21 symbols
model_en.py3 symbols
model.py3 symbols
static/js/zzsc.js1 symbols
manage.py1 symbols

For agents

$ claude mcp add WantWords \
  -- python -m otcore.mcp_server <graph>

⬇ download graph artifact