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two simple typos fixed #861

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two simple typos fixed #861

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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -521,8 +521,8 @@ Main Functions
1. Cut
--------
* The `jieba.cut` function accepts three input parameters: the first parameter is the string to be cut; the second parameter is `cut_all`, controlling the cut mode; the third parameter is to control whether to use the Hidden Markov Model.
* `jieba.cut_for_search` accepts two parameter: the string to be cut; whether to use the Hidden Markov Model. This will cut the sentence into short words suitable for search engines.
* The input string can be an unicode/str object, or a str/bytes object which is encoded in UTF-8 or GBK. Note that using GBK encoding is not recommended because it may be unexpectly decoded as UTF-8.
* `jieba.cut_for_search` accepts two parameters: the string to be cut; whether to use the Hidden Markov Model. This will cut the sentence into short words suitable for search engines.
* The input string can be a unicode/str object, or a str/bytes object which is encoded in UTF-8 or GBK. Note that using GBK encoding is not recommended because it may be unexpectly decoded as UTF-8.
* `jieba.cut` and `jieba.cut_for_search` returns an generator, from which you can use a `for` loop to get the segmentation result (in unicode).
* `jieba.lcut` and `jieba.lcut_for_search` returns a list.
* `jieba.Tokenizer(dictionary=DEFAULT_DICT)` creates a new customized Tokenizer, which enables you to use different dictionaries at the same time. `jieba.dt` is the default Tokenizer, to which almost all global functions are mapped.
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