Python: Split unicode string on word boundaries

Question

I need to take a string, and shorten it to 140 characters.

Currently I am doing:

if len(tweet) > 140:
    tweet = re.sub(r"\s+", " ", tweet) #normalize space
    footer = "… " + utils.shorten_urls(post['url'])
    avail = 140 - len(footer)
    words = tweet.split()
    result = ""
    for word in words:
        word += " "
        if len(word) > avail:
            break
        result += word
        avail -= len(word)
    tweet = (result + footer).strip()
    assert len(tweet) <= 140

So this works great for English, and English like strings, but fails for a Chinese string because tweet.split() just returns one array:

>>> s = u"简讯：新華社報道，美國總統奧巴馬乘坐的「空軍一號」專機晚上10時42分進入上海空域，預計約30分鐘後抵達浦東國際機場，開展他上任後首次訪華之旅。"
>>> s
u'\u7b80\u8baf\uff1a\u65b0\u83ef\u793e\u5831\u9053\uff0c\u7f8e\u570b\u7e3d\u7d71\u5967\u5df4\u99ac\u4e58\u5750\u7684\u300c\u7a7a\u8ecd\u4e00\u865f\u300d\u5c08\u6a5f\u665a\u4e0a10\u664242\u5206\u9032\u5165\u4e0a\u6d77\u7a7a\u57df\uff0c\u9810\u8a08\u7d0430\u5206\u9418\u5f8c\u62b5\u9054\u6d66\u6771\u570b\u969b\u6a5f\u5834\uff0c\u958b\u5c55\u4ed6\u4e0a\u4efb\u5f8c\u9996\u6b21\u8a2a\u83ef\u4e4b\u65c5\u3002'
>>> s.split()
[u'\u7b80\u8baf\uff1a\u65b0\u83ef\u793e\u5831\u9053\uff0c\u7f8e\u570b\u7e3d\u7d71\u5967\u5df4\u99ac\u4e58\u5750\u7684\u300c\u7a7a\u8ecd\u4e00\u865f\u300d\u5c08\u6a5f\u665a\u4e0a10\u664242\u5206\u9032\u5165\u4e0a\u6d77\u7a7a\u57df\uff0c\u9810\u8a08\u7d0430\u5206\u9418\u5f8c\u62b5\u9054\u6d66\u6771\u570b\u969b\u6a5f\u5834\uff0c\u958b\u5c55\u4ed6\u4e0a\u4efb\u5f8c\u9996\u6b21\u8a2a\u83ef\u4e4b\u65c5\u3002']

How should I do this so it handles I18N? Does this make sense in all languages?

I'm on python 2.5.4 if that matters.

Answer 1

Chinese doesn't usually have whitespace between words, and the symbols can have different meanings depending on context. You will have to understand the text in order to split it at a word boundary. In other words, what you are trying to do is not easy in general.

Answer 2

For word segmentation in Chinese, and other advanced tasks in processing natural language, consider NLTK as a good starting point if not a complete solution -- it's a rich Python-based toolkit, particularly good for learning about NL processing techniques (and not rarely good enough to offer you viable solution to some of these problems).