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. link to paper

In this library, we provide a pre-trained model that can:

• given two Chinese phrases (of the same length), return the phonetic distance of the input phrases.
• given a Chinese phrase, return its top-k similar (phoentically) Chinese phrases.

Dependencies: We use pypinyin to convert Chinese characters into their correponding pinyins for internal computation. Install pypinyin before use our library

There are two ways to install this library:

• Download the source code from the code folder and compile it yourself.
• run pip install dimsim (pending, needs to upload to pypi)

Computing phonetic distance of two Chinese phrases

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

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

Return top-k phonetically similar phrases of a given Chinese phrase. Two parameters:

• model 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.

candidates = getCandidates("大侠", model="simplified", theta=1)
['打下', '大虾', '大侠']

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