delete unnecessary information in README.md

README.md

@@ -15,27 +15,20 @@ This tool has the following features.
 - The POS-tagging model uses tag dictionary information [[Inoue+]](http://www.aclweb.org/anthology/K17-1042).
 
 For more details refer to the following links.
-- The Slide in Japanese is available [here](https://drive.google.com/open?id=1AzR5wh5502u_OI_Jxwsq24t-er_rnJBP).
-- The Documentation is available [here](https://nagisa.readthedocs.io/en/latest/?badge=latest).
+- The slide in Japanese is available [here](https://drive.google.com/open?id=1AzR5wh5502u_OI_Jxwsq24t-er_rnJBP).
+- The documentation is available [here](https://nagisa.readthedocs.io/en/latest/?badge=latest).
 
 Installation
 =============
 
 Nagisa is compatible with: Python 2.7-3.6.  
 This tool uses [DyNet](https://github.com/clab/dynet) (the Dynamic Neural Network Toolkit) to calcucate neural networks.
 
-- Using pip
+- You can install nagisa by using the following command.
 ```bash
 pip install nagisa
 ```
 
-- From github
-```bash
-git clone https://github.com/taishi-i/nagisa
-cd nagisa
-python setup.py install
-```
-
 Usage
 ======
 
@@ -62,53 +55,3 @@ print(words)
 #=> ['Python', 'で', '簡単', 'に', '使える', 'ツール', 'です']
 ```
 
-Functions
-=========
-
-- Output words can be controlled by POS-tags.
-```python
-# Extarcting all nouns from a text
-words = nagisa.extract(text, extract_postags=['名詞'])
-print(words) 
-#=> Python/名詞 ツール/名詞
-
-# Filtering specific POS-tags from a text
-words = nagisa.filter(text, filter_postags=['助詞', '助動詞'])
-print(words) 
-#=> Python/名詞 簡単/形状詞 使える/動詞 ツール/名詞
-
-# A list of available POS-tags
-print(nagisa.tagger.postags) 
-#=> ['補助記号', '名詞', ... , 'URL']
-
-```
-
-- A word can be recognized as a single word forcibly.
-```python
-text = 'ニューラルネットワークを使ってます。'
-print(nagisa.tagging(text)) 
-#=> ニューラル/名詞 ネットワーク/名詞 を/助詞 使っ/動詞 て/助動詞 ます/助動詞 。/補助記号
-
-# If a word is included in the single_word_list, it is recognized as a single word. 
-tagger_nn = nagisa.Tagger(single_word_list=['ニューラルネットワーク'])
-print(tagger_nn.tagging(text)) 
-#=> ニューラルネットワーク/名詞 を/助詞 使っ/動詞 て/助動詞 ます/助動詞 。/補助記号
-```
-
-- Nagisa is good at capturing URLs and emoticons from an input text.
-```python
-text = '(人•ᴗ•♡)こんばんは♪'
-words = nagisa.tagging(text)
-print(words) 
-#=> (人•ᴗ•♡)/補助記号 こんばんは/感動詞 ♪/補助記号
-
-url = 'https://github.com/taishi-i/nagisaでコードを公開中(๑¯ω¯๑)'
-words = nagisa.tagging(url) 
-print(words) 
-#=> https://github.com/taishi-i/nagisa/URL で/助詞 コード/名詞 を/助詞 公開/名詞 中/接尾辞 (๑　̄ω　̄๑)/補助記号
-
-words = nagisa.filter(url, filter_postags=['URL', '補助記号', '助詞'])
-print(words) 
-#=> コード/名詞 公開/名詞 中/接尾辞
-```
-