用于从文本中提取动作的 NLP

Question

我希望有人能为我指明正确的方向，以学习如何从一堆文本中分离出动作。

假设我有这个文本，

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.

我试图将其拆分为

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.

我无法在搜索中找到任何专门基于操作的内容。它需要比仅仅挑选动词更聪明，因为有时有多个动词与一个动作相关联，例如第二个项目有“go”、“pick-up”和“get”，但这只是全部的一部分单个动作的。当然，“蒂姆的飞行”仅表示现在分词的动作，动词接近该句段的末尾。

关于在哪里做这种事情有什么建议吗？注意事项、推荐读物等

Answer 1

简单的方法：使用[您最喜欢的解析器]解析文本，然后选择处于祈使语气的句子或SBAR短语。 Stanford Parser 恰好有“改进对命令式的识别”最新版本。

除了标准解析器程序中已经包含的内容之外，可能不需要机器学习。

Answer 2

该域称为信息提取。

句子理解的一般方法是：

• 提取词性标记的解析树（Python spaCy.io、nltk、CoreNLP 等）
• 提取词向量（例如 word2vec）