DimSim is a library developed by the Scalable Knowledge Intelligence team at IBM Almaden Research Center as part of the SystemT project.

The PyPi project page can be found here. It was created in collaboration with IBM Center for Open-Source Data and AI Technologies (CODAIT).

We provide a phonetic algorithm for indexing Chinese characters by sound. The technical details can be found in the following paper:

Min Li, Marina Danilevsky, Sara Noeman and Yunyao Li. DIMSIM: An Accurate Chinese Phonetic Similarity Algorithm based on Learned High Dimensional Encoding. CoNLL 2018.

In this library, we provide a pre-trained model that can perform the following functions, in compliance with the phonetic principles of Mandarin Chinese as guided by the Romanization defined in ISO 7098:2015:

• Given two Chinese phrases (of the same length), return the phonetic distance of the input phrases. Optionally you can feed in pinyin strings of Chinese phrases too.
• Given a Chinese phrase, return its top-k similar (phoentically) Chinese phrases.

Dependencies:

• pypinyin: used for translating Chinese characters into their correponding pinyins.

There are two ways to install this library:

• Install from PyPi

pip install dimsim

• Download the source code by cloning this repo and compile it yourself.

git clone [email protected]:System-T/DimSim.git

cd DimSim/

pip install -e .

Once you have the package installed you can use it for the two functions as shown below.

• Computing phonetic distance of two Chinese phrases. The optional argument pinyin (False by default) can be used to provide a pinyin string list directly. See example usage below.

import dimsim

dist = dimsim.get_distance("大侠","大虾")
0.0002380952380952381

dist = dimsim.get_distance("大侠","大人")
25.001417183349876

dist = dimsim.get_distance(['da4','xia2'],['da4','xia1']], pinyin=True)
0.0002380952380952381

dist = dimsim.get_distance(['da4','xia2'],['da4','ren2']], pinyin=True)
25.001417183349876

• Return top-k phonetically similar phrases of a given Chinese phrase. Two parameters:
• mode controls the character type of the returned Chinese phrases, where 'simplified' represents simplified Chinese and 'traditional' represents traditional Chinese.
• theta controls the size of search space for the candidate phrases.

import dimsim

candidates = dimsim.get_candidates("大侠", mode="simplified", theta=1)

['打下', '大虾', '大侠']

candidates = dimsim.get_candidates("粉丝", mode="traditinoal", theta=1)
['門市', '分時', '焚屍', '粉飾', '粉絲']

Return Pinyin RCode

rcode = getRCode("海底小纵队")
"hai'di'xiao'zong'dui"

rcode = getRCode("海底小中队")
"hai'di'xiao'zong'dui"

Please cite the library by referencing the published paper:

@InProceedings{K18-1043,
  author = 	{Li, Min and Danilevsky, Marina and Noeman, Sara and Li, Yunyao},
  title = 	{{DIMSIM:} An Accurate Chinese Phonetic Similarity Algorithm Based on Learned High Dimensional Encoding},
  booktitle = 	{Proceedings of the 22nd Conference on Computational Natural Language Learning},
  year = 	{2018},
  publisher = 	{Association for Computational Linguistics},
  pages = 	{444-453},
  location = 	{Brussels, Belgium},
  url = 	{http://aclweb.org/anthology/K18-1043}
}