Word-Context Character Embeddings for Chinese word Segmentation

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This is the code for the paper "Word-Based Character Embeddings for Chinese word Segmentation".

Source

Our model could be divided into 2 aspects:

• baseline segmentation model is in the "segmenter" directory.
• word-based character embedding training code is in the "modified-word2vec" dircectory.

Reguired Softwares

• CMake
• Boost
• glog

Embedding Training

#Run
make


#Training command

./seg2vec -train data/small.seg.char.giga \
	-output  vecs/example.emb \
	-cbow 0  \
	-size 50 \
	-window 5 \
	-negative 10 \
	-sample 1e-4 \
	-threads 6 \
	-binary 0 \
	-iter 8 \

Segmentation Model

./configure
make

mkdir -p model
mkdir -p log

GLOG_log_dir=log ./greedy --cnn-mem 999 -i 1 \
	-T ../../data/pku/small.train.seg \
	-d ../../data/pku/small.dev.seg \
	-t ../../data/pku/small.test.seg \
	--optimizer simple_sgd \
	--evaluate_stops 2500 \
	--outfile ctb.test.res \

Segmentation Parameters

./greedy -h

Notes:

• You can find the sample training data in the data directory.
• The performances of crf and greedy models are comparable.

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[1]: Hao Zhou, Zhenting Yu, Yue Zhang, Shujian Huang, Xinyu Dai and Jiajun Chen. Word-Context Character Embeddings for Chinese word Segmentation. In Proceeding of EMNLP 2017, short paper.