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        "author": "IBM SystemT, IBM CODAIT",
        "author_email": "qian.kun@ibm.com, karthik.muthuraman@ibm.com, ihjhuo@ibm.com, frreiss@us.ibm.com",
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        "description": "# DimSim - A Chinese Soundex Library (Python version) \n\nDimSim is a library developed by the Scalable Knowledge Intelligence team at IBM Almaden Research Center as part of the [SystemT](https://researcher.watson.ibm.com/researcher/view_group.php?id=1264) project. \n\nThe PyPi project page can be found [here](https://pypi.org/project/dimsim/). It was created in collaboration with IBM Center for Open-Source Data and AI Technologies ([CODAIT](codait.org)).\n\n## Overview\nWe provide a phonetic algorithm for indexing Chinese characters by sound. The technical details can be found in the following [paper](http://aclweb.org/anthology/K18-1043):\n\nMin Li, Marina Danilevsky, Sara Noeman and Yunyao Li. *DIMSIM: An Accurate Chinese Phonetic Similarity Algorithm based on Learned High Dimensional Encoding*. CoNLL 2018.\n\nIn this library, we provide a pre-trained model that can perform the following functions:\n- 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.\n- Given a Chinese phrase, return its top-k similar (phoentically) Chinese phrases.\n\n\n\n## How to install\n\n**Dependencies**:\n- [pypinyin](https://github.com/mozillazg/python-pinyin): used for translating Chinese characters into their correponding pinyins. \n\nThere are two ways to install this library:\n- Install from PyPi\n\n```shell\npip install dimsim\n```\n- Download the source code by cloning this repo and compile it yourself.\n\n```shell\ngit clone git@github.com:System-T/DimSim.git\n\ncd DimSim/\n\npip install -e .\n```\n\n## How to use\nOnce you have the package installed you can use it for the two functions as shown below.\n\n- 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.\n\n```python\nimport dimsim\n\ndist = dimsim.get_distance(\"\u5927\u4fa0\",\"\u5927\u867e\")\n0.0002380952380952381\n\ndist = dimsim.get_distance(\"\u5927\u4fa0\",\"\u5927\u4eba\")\n25.001417183349876\n\ndist = dimsim.get_distance(['da4','xia2'],['da4','xia1']], pinyin=True)\n0.0002380952380952381\n\ndist = dimsim.get_distance(['da4','xia2'],['da4','ren2']], pinyin=True)\n25.001417183349876\n\n```\n***\n- Return top-k phonetically similar phrases of a given Chinese phrase. Two parameters:\n- **mode** controls the character type of the returned Chinese phrases, where 'simplified' represents simplified Chinese and 'traditional' represents traditional Chinese.\n- **theta** controls the size of search space for the candidate phrases.\n```python\nimport dimsim\n\ncandidates = dimsim.get_candidates(\"\u5927\u4fa0\", mode=\"simplified\", theta=1)\n['\u6253\u4e0b', '\u5927\u867e', '\u5927\u4fa0']\n\ncandidates = dimsim.get_candidates(\"\u7c89\u4e1d\", mode=\"traditinoal\", theta=1)\n['\u9580\u5e02', '\u5206\u6642', '\u711a\u5c4d', '\u7c89\u98fe', '\u7c89\u7d72']\n```\n\n## Citation\n\nPlease cite the library by referencing the published paper:\n```\n@InProceedings{K18-1043,\n  author = \t{Li, Min and Danilevsky, Marina and Noeman, Sara and Li, Yunyao},\n  title = \t{{DIMSIM:} An Accurate Chinese Phonetic Similarity Algorithm Based on Learned High Dimensional Encoding},\n  booktitle = \t{Proceedings of the 22nd Conference on Computational Natural Language Learning},\n  year = \t{2018},\n  publisher = \t{Association for Computational Linguistics},\n  pages = \t{444-453},\n  location = \t{Brussels, Belgium},\n  url = \t{http://aclweb.org/anthology/K18-1043}\n}\n```\n\n\n",
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