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    "info": {
        "author": "Lapis-Hong",
        "author_email": "dinghongquan@sjtu.edu.cn",
        "bugtrack_url": null,
        "classifiers": [
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
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        "description": "# xinci \u65b0\u8bcd & \u62bd\u8bcd\nxinci is a Python interface for chinese words extraction & new words extraction.\n[https://pypi.org/project/xinci/]\n\n## Requirements\nPython >= 2.7\n\n## Installation\n### 1. using pip\n```shell\npip install xinci\n```\n### 2. using setup.py\n``` shell\ngit clone git@github.com:Lapis-Hong/xinci.git  \ncd xinci \npip setup.py install\n```\n\n## Usage\nThis package has two main use cases: words extraction and\nfind new words. \n\n### 1. command line\n```shell\ncd xinci\npython word_extraction.py \n```\nor \n```\n./run.sh\n```\n\n### 2. python package\n```python \nimport xinci\n\n# if you want to see logging events.\nimport logging\nlogging.basicConfig(level=logging.INFO, format='%(asctime)s : %(levelname)s : %(message)s')\n\n# init default dictionary or user dic,\ndic = xinci.Dictionary()\n# load vocab, vocab is a python set.\nvocab = dic.load()  # or dic.dictionary\nprint(vocab)\n\n# add words to dic\ndic.add(['\u795e\u9a6c'])  # or dic.add_from_file('user.dic')\n# remove words from dic\ndic.remove(['\u795e\u9a6c'])  # or dic.remove_from_file('user.dic')\n\n# extract new words, xc is a set\nxc = xinci.extract('corpus.txt')\nfor w in xc:\n    print(w)\n# extract all words, c is a set\nc = xinci.extract('corpus.txt', all_words=True)\nfor w in xc:\n    print(w)\n```\nresult\n```angular2html\n\u53d1\u73b05\u4e2a\u65b0\u8bcd\u5982\u4e0b:\n@\u65b0\u8bcd\t@\u8bcd\u9891\n\u795b\u6591\t13\n\u540e\u518d\t7\n\u4eca\u65e5\u5934\u6761\t9\n\u6d17\u51c0\u5207\t7\n\u86cb\u6db2\t9\n```\n### Notes: Iteratively add \"not seems to new words\" in result to common dic will improve a lot. \n\n\n## API documentation\n```python\nxc = xinci.extract(params)\n\n```\nList of available `params` and their default value:\n```angular2html\ncorpus_file:           string, input corpus file (required)\ncommon_words_file:     string, common words dic file [common.dic]\nmin_candidate_len:     int, min candidate word length [2]\nmax_candidate_len:     int, max candidate word length [5]\nleast_cnt_threshold:   int, least word count to extract [5]\nsolid_rate_threshold:  float, solid rate threshold [0.018]\nentropy_threshold:     float, entropy threshold [1.92]\nall_words: bool,       set True to extract all words mode [False]\nsave_file: string,     output file [None]\n```\n\n## References\nThe code is based on this java version\n[https://github.com/GeorgeBourne/grid]\n\n\n\n",
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        "keywords": "NLP,Chinese words extraction,New words discovery",
        "license": "MIT",
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        "name": "xinci",
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        "summary": "Chinese words extraction and new words discovery",
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