JointPS
A re-implementation of A Simple and Effective Neural Model for Joint Word Segmentation and POS Tagging based on PyTorch.
The C++ Code [zhangmeishan/NNTranJSTagger].
PyTorch-0.3.1 Code release on here. [PyTorch-0.3.1]
Requirement
pip3 install -r requirements.txt
Python == 3.6
PyTorch == 1.0.1
Usage
modify the config file, detail see the Config directory
Train:
(1) sh run_train_p.sh
(2) python -u main.py --config ./Config/config.cfg --device cuda:0--train -p
[device: "cpu", "cuda:0", "cuda:1", ......]
Config
optimizer: Adam
lr: 0.001
dropout: 0.25
embed_char_dim: 200
embed_bichar_dim: 200
rnn_dim: 200
rnn_hidden_dim: 200
pos_dim: 100
oov: avg
Refer to config.cfg file for more details.
- final log in [final_log]
Network Structure
Performance
| CTB5 | CTB6 | CTB7 | PKU | NCC | |
|---|---|---|---|---|---|
| Model | SEG POS | SEG POS | SEG POS | SEG POS | SEG POS |
| Our Model (No External Embeddings) | 97.69 94.16 | 95.37 90.83 | 95.32 90.25 | 95.22 92.62 | 93.97 89.47 |
| Our Model (Basic Embeddings) | 97.93 94.44 | 95.78 91.79 | 95.77 91.12 | 95.82 93.42 | 94.52 89.82 |
| Our Model (Word-context Embeddings) | 98.50 94.95 | 96.36 92.51 | 96.25 91.87 | 96.35 94.14 | 95.30 90.42 |
Cite
@Article{zhang2018jointposseg,
author = {Zhang, Meishan and Yu, Nan and Fu, Guohong},
title = {A Simple and Effective Neural Model for Joint Word Segmentation and POS Tagging},
journal = {IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)},
year = {2018},
volume = {26},
number = {9},
pages = {1528--1538},
publisher = {IEEE Press},
}
Question
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if you have any question, you can open a issue or email [email protected]、[email protected]、bamtercelboo@{gmail.com, 163.com}.
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if you have any good suggestions, you can PR or email me.