Pointwise Hilbert–Schmidt Independence Criterion (PHSIC)

PHSIC computes co-occurence strength between sentences with short learning time.

For example, given consistent sentence pairs:

PHSIC can give high scores to consistent pairs in terms of the given pairs:

The details can be found in our paper:

Sho Yokoi, Sosuke Kobayashi, Kenji Fukumizu, Jun Suzuki, Kentaro Inui. Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.1763–1775 , 2018.

Installation

$ pip install phsic-cli

This will install phsic command to your environment:

$ phsic --help

Basic Usage

Download pre-trained wordvecs (e.g. fasttext):

$ wget https://s3-us-west-1.amazonaws.com/fasttext-vectors/crawl-300d-2M.vec.zip
$ unzip crawl-300d-2M.vec.zip

Prepare dataset:

$ 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

Then, train and predict:

$ 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

Citation

If you make use of this software we would appreciate it if you would cite the paper:

@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"
}