cw2vec: A C++ repository from cjm1044642385

Introduction

Paper Link: cw2vec: Learning Chinese Word Embeddings with Stroke n-gram Information

Requirements

cmake version 3.10.0-rc5
make GNU Make 4.1
gcc version 5.4.0

Run Demo

I have uploaded word2vec binary executable file in cw2vec/word2vec/bin and rewrite run.sh for simple test, you can run run.sh directly for simple test.
According to the Building cw2vec using cmake to recompile and run other model with the Example use cases.

Building cw2vec using cmake

git clone git@github.com:bamtercelboo/cw2vec.git
cd cw2vec && cd word2vec && cd build
cmake ..
make
cd ../bin

This will create the word2vec binary and also all relevant libraries.

Example use cases

the repo not only implement cw2vec(named substoke), but also the skipgram, cbow of word2vec, furthermore, fasttext skipgram is implemented(named subword).

Please modify train.txt and feature.txt into your own train document.

skipgram: ./word2vec skipgram -input train.txt -output skipgram_out -lr 0.025 -dim 100 -ws 5 -epoch 5 -minCount 10 -neg 5 -loss ns -thread 8 -t 1e-4 -lrUpdateRate 100  

cbow:     ./word2vec cbow -input train.txt -output cbow_out -lr 0.05 -dim 100 -ws 5 -epoch 5 -minCount 10 -neg 5 -loss ns -thread 8 -t 1e-4 -lrUpdateRate 100

subword:  ./word2vec subword -input train.txt -output subword_out -lr 0.025 -dim 100 -ws 5 -epoch 5 -minCount 10 -neg 5 -loss ns -minn 3 -maxn 6 -thread 8 -t 1e-4 -lrUpdateRate 100

substoke: ./word2vec substoke -input train.txt -infeature feature.txt -output substoke_out -lr 0.025 -dim 100 -ws 5 -epoch 5 -minCount 10 -neg 5 -loss ns -minn 3 -maxn 18 -thread 8 -t 1e-4 -lrUpdateRate 100

Get chinese stoke feature

substoke model need chinese stoke feature(-infeature)，I have written a script to acquire the Chinese character of stroke information from handian. here is the script extract_zh_char_stoke, see the readme for details.

feature file(feature.txt) like this:

中 丨フ一丨
国 丨フ一一丨一丶一
庆 丶一ノ一ノ丶
假 ノ丨フ一丨一一フ一フ丶
期 一丨丨一一一ノ丶ノフ一一
香 ノ一丨ノ丶丨フ一一
江 丶丶一一丨一
将 丶一丨ノフ丶一丨丶
涌 丶丶一フ丶丨フ一一丨
入 ノ丶
人 ノ丶
潮 丶丶一一丨丨フ一一一丨ノフ一一
......

I provided a feature file for the test，path is sample/substoke_feature.txt.

Substoke model output embeddings

In this paper, the context word embeddings is used directly as the final word vector. However, according to the idea of fasttext, I also take into account the n-gram feature vector of the stroke information, the n-gram feature vector of the stroke information is taken as an average substitute for the word vector.
There are two outputs in substoke model:
- output ends with vec is the context word vector.
- output ends with avg is the n-gram feature vector average.

Word similarity evaluation

1. Evaluation script

I have already written a Chinese word similarity evaluation script. Chinese-Word-Similarity-and-Word-Analogy, see the readme for details.

2. Parameter Settings

The parameters are set as follows:

dim  100
window sizes  5
negative  5
epoch  5
minCount  10
lr  skipgram(0.025)，cbow(0.05)，substoke(0.025)
n-gram  minn=3, maxn=18

3. result

Experimental results show follows

Full documentation

Invoke a command without arguments to list available arguments and their default values:

./word2vec 
usage: word2vec <command> <args>
The commands supported by word2vec are:

skipgram  ------ train word embedding by use skipgram model
cbow      ------ train word embedding by use cbow model
subword   ------ train word embedding by use subword(fasttext skipgram)  model
substoke  ------ train chinses character embedding by use substoke(cw2vec) model

./word2vec substoke -h
Train Embedding By Using [substoke] model
Here is the help information! Usage:

The Following arguments are mandatory:
	-input              training file path
	-infeature          substoke feature file path
	-output             output file path

The Following arguments are optional:
	-verbose            verbosity level[2]

The following arguments for the dictionary are optional:
	-minCount           minimal number of word occurences default:[10]
	-bucket             number of buckets default:[2000000]
	-minn               min length of char ngram default:[3]
	-maxn               max length of char ngram default:[6]
	-t                  sampling threshold default:[0.001]

The following arguments for training are optional:
	-lr                 learning rate default:[0.05]
	-lrUpdateRate       change the rate of updates for the learning rate default:[100]
	-dim                size of word vectors default:[100]
	-ws                 size of the context window default:[5]
	-epoch              number of epochs default:[5]
	-neg                number of negatives sampled default:[5]
	-loss               loss function {ns} default:[ns]
	-thread             number of threads default:[1]
	-pretrainedVectors  pretrained word vectors for supervised learning default:[]
	-saveOutput         whether output params should be saved default:[false]

References

[1] Cao, Shaosheng, et al. "cw2vec: Learning Chinese Word Embeddings with Stroke n-gram Information." (2018).
[2] Bojanowski, Piotr, et al. "Enriching word vectors with subword information." arXiv preprint arXiv:1607.04606 (2016).
[3] fastText-github
[4] cw2vec理论及其实现

Question

if you have any question, you can open a issue or email bamtercelboo@{gmail.com, 163.com}.
if you have any good suggestions, you can PR or email me.

cjm1044642385/cw2vec