用於從文本中提取動作的NLP

我希望有人能指引我朝着正確的方向學習如何從一堆文本中分離出動作。用於從文本中提取動作的NLP

假設我有這樣的文字

 
Drop off the dry cleaning, and go to the corner store and pick-up a jug of milk and get a pint of strawberries. 
Then, go pick up the kids from school. First, get John who is in the daycare next to the library, and then get Sam who is two blocks away. 
By the time you've got the kids, you'll need to stop by the doctors office for the perscription. Tim's flight arrives at 4pm. 
It's American Airlines flight 331 arriving from Dallas. It will be getting close to rush hour, so make sure you leave yourself enough time.

我試圖把它分成

 
Drop off the dry cleaning,

 
and go to the corner store and pick-up a jug of milk and get a pint of strawberries.

 
Then, go pick up the kids from school. First, get John who is in the daycare next to the library, and then get Sam who is two blocks away.

 
By the time you've got the kids, you'll need to stop by the doctors office for the perscription.

 Tim's flight arrives at 4pm. 
It's American Airlines flight 331 arriving from Dallas. It will be getting close to rush hour, so make sure you leave yourself enough time.

我沒有蜜蜂能夠在我的搜索中找到任何特別基於操作的內容。它需要比選擇動詞更聰明，因爲有多個動詞有時與一個動作相關聯，例如第二個項目具有「去」，「拾取」和「獲取」，但這都是部分單一行動。當然，「蒂姆的飛行」只是用現在分詞來表示一個動作，而動詞則要走到該段的末尾。

任何建議在哪裏看這樣的事情？注意事項，推薦讀物等等。

來源

2011-11-18 pedalpete

您確定這是提問這個問題的正確網站嗎？ –

你會推薦另一個網站？有NLP社區嗎？或者問題提出的方式，而不是你想知道@alexander的內容？ – pedalpete

[提取'有用'信息的句子？]（http://stackoverflow.com/questions/6482152/extracting-useful-information-out-of-sentences），它會詢問關於Python NLTK的可能的重複。 – smci

簡單的方法：使用[您最喜愛的解析器]解析文本，然後選擇句子或SBAR處於命令性情緒的短語。 Stanford Parser在其最新版本中恰好有「改進對命令的識別」。

除了已經包含在標準解析器程序中的機器學習可能沒有必要。

來源

2011-11-18 14:45:27

該域名爲Information Extraction。

的一般方法來句的理解可以是：

提取零件的詞性標記解析樹（Python的spaCy.io，NLTK，CoreNLP等）
提取字矢量（例如word2vec）

來源

2016-10-12 20:04:43 smci

用於從文本中提取動作的NLP

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