#注意

• Equipped with Emoticon detection

Emoticons will not be segmented as sequences of meaningless punctuations.

• Data are trained with Sinica Corpus

Results are more accurate when dealing with Traditional Chinese (F1-score = 0.91).

• Using Brill Tagger

Training data are trained with Sinica Treebank, which raises the accuracy of POS tagging.

Print out without POS tagging:

print result.nopos

Result:

台灣 大學 語言學 研究所 LOPE 實驗室 超強
Taco 門神 超罩
Amber 和 Emily 是 雙胞胎
Yvonne 不 是 小老鼠
期末 要 爆炸 啦 ！ ◢▆▅▄▃崩╰(〒皿〒)╯潰▃▄▅▇◣ 

If you want the result to be in a list, set mode to list:

###Add user defined dictionary

jieba.add_guaranteed_wordlist(lst)

lst should be a list of unicodes, e.g., [u'蟹老闆', u'張他口', u'愛米粒', u'劉阿吉']