fix English part of README

README.md

@@ -400,20 +400,22 @@ Main Functions
 
 **Code example: segmentation**
 
-    #encoding=utf-8
-    import jieba
+```python
+#encoding=utf-8
+import jieba
 
-    seg_list = jieba.cut("我来到北京清华大学", cut_all=True)
-    print "Full Mode:", "/ ".join(seg_list)  # 全模式
+seg_list = jieba.cut("我来到北京清华大学", cut_all=True)
+print "Full Mode:", "/ ".join(seg_list)  # 全模式
 
-    seg_list = jieba.cut("我来到北京清华大学", cut_all=False)
-    print "Default Mode:", "/ ".join(seg_list)  # 默认模式
+seg_list = jieba.cut("我来到北京清华大学", cut_all=False)
+print "Default Mode:", "/ ".join(seg_list)  # 默认模式
 
-    seg_list = jieba.cut("他来到了网易杭研大厦")
-    print ", ".join(seg_list)
+seg_list = jieba.cut("他来到了网易杭研大厦")
+print ", ".join(seg_list)
 
-    seg_list = jieba.cut_for_search("小明硕士毕业于中国科学院计算所，后在日本京都大学深造")  # 搜索引擎模式
-    print ", ".join(seg_list)
+seg_list = jieba.cut_for_search("小明硕士毕业于中国科学院计算所，后在日本京都大学深造")  # 搜索引擎模式
+print ", ".join(seg_list)
+```
 
 Output:
 
@@ -451,7 +453,7 @@ Output:
 
 Example (keyword extraction)
 
-    https://github.com/fxsjy/jieba/blob/master/test/extract_tags.py
+https://github.com/fxsjy/jieba/blob/master/test/extract_tags.py
 
 Developers can specify their own custom IDF corpus in jieba keyword extraction
 
@@ -474,15 +476,17 @@ Use: `jieba.analyse.textrank(raw_text)`.
 * Tags the POS of each word after segmentation, using labels compatible with ictclas.
 * Example:
 
-    >>> import jieba.posseg as pseg
-    >>> words = pseg.cut("我爱北京天安门")
-    >>> for w in words:
-    ...    print w.word, w.flag
-    ...
-    我 r
-    爱 v
-    北京 ns
-    天安门 ns
+```pycon
+>>> import jieba.posseg as pseg
+>>> words = pseg.cut("我爱北京天安门")
+>>> for w in words:
+...    print w.word, w.flag
+...
+我 r
+爱 v
+北京 ns
+天安门 ns
+```
 
 5) : Parallel Processing
 -----------