点态希尔伯特-施密特独立准则（PHSIC）
phsic-cli的Python项目详细描述
点态hilbert窶鉄chmidt独立准则（phsic）
利用相似性计算两个对象之间的共现。
例如，给定一致的句子对：
| X | Y |
|---|---|
| They had breakfast at the hotel. | They are full now. |
| They had breakfast at ten. | I'm full. |
| She had breakfast with her friends. | She felt happy. |
| They had breakfast with their friends at the Japanese restaurant. | They felt happy. |
| He have trouble with his homework. | He cries. |
| I have trouble associating with others. | I cry. |
PHSIC可以根据给定的配对给一致的配对以高分：
| X | Y | score |
|---|---|---|
| They had breakfast at the hotel. | They are full now. | 0.1134 |
| They had breakfast at an Italian restaurant. | They are stuffed now. | 0.0023 |
| I have dinner. | I have dinner again. | 0.0023 |
安装
$ pip install phsic
这将在您的环境中安装phsic命令：
$ phsic --help
基本用法
下载预先训练过的WordVecs（如FastText）：
$ wget https://s3-us-west-1.amazonaws.com/fasttext-vectors/crawl-300d-2M.vec.zip
$ unzip crawl-300d-2M.vec.zip
准备数据集：
$ TAB="$(printf '\t')"
$ cat << EOF > train.txt
They had breakfast at the hotel.${TAB}They are full now.
They had breakfast at ten.${TAB}I'm full.
She had breakfast with her friends.${TAB}She felt happy.
They had breakfast with their friends at the Japanese restaurant.${TAB}They felt happy.
He have trouble with his homework.${TAB}He cries.
I have trouble associating with others.${TAB}I cry.
EOF
$ cut -f 1 train.txt > train_X.txt
$ cut -f 2 train.txt > train_Y.txt
$ cat << EOF > test.txt
They had breakfast at the hotel.${TAB}They are full now.
They had breakfast at an Italian restaurant.${TAB}They are stuffed now.
I have dinner.${TAB}I have dinner again.
EOF
$ cut -f 1 test.txt > test_X.txt
$ cut -f 2 test.txt > test_Y.txt
然后，训练并预测：
$ phsic train_X.txt train_Y.txt --kernel1 Gaussian 1.0 --encoder1 SumBov FasttextEn --emb1 crawl-300d-2M.vec --kernel2 Gaussian 1.0 --encoder2 SumBov FasttextEn --emb2 crawl-300d-2M.vec --limit_words1 10000 --limit_words2 10000 --dim1 3 --dim2 3 --out_prefix toy --out_dir out --X_test test_X.txt --Y_test test_Y.txt
$ cat toy.Gaussian-1.0-SumBov-FasttextEn.Gaussian-1.0-SumBov-FasttextEn.3.3.phsic
1.134489336180434238e-01
2.320408776101631244e-03
2.321869174772554344e-03
引文
@InProceedings{D18-1203,
author = "Yokoi, Sho
and Kobayashi, Sosuke
and Fukumizu, Kenji
and Suzuki, Jun
and Inui, Kentaro",
title = "Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
year = "2018",
publisher = "Association for Computational Linguistics",
pages = "1763--1775",
location = "Brussels, Belgium",
url = "http://aclweb.org/anthology/D18-1203"
}
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