2
votes

My documents often include sentences like:

Had I known about this, I would have prevented this problem

or

If John was informed, this wouldn't happen

or

this wouldn't be a problem if Jason was smart

I'm interested in extracting these sort of information (not sure what they are called, linguistically). So I would like to extract either the whole sentence, or ideally, a summary like:

(inform John) (prevent)

Most, if not all, the examples of relation extraction, and information extraction that I've come across, follow fairly standard flow: do NER, then relation extraction looks for relations like "in" or "at", etc (ch7 of nltk book for example).

Do these type of sentences fall under a certain category in NLP? Are there any papers/tutorials on something like this?

1

1 Answers

2
votes

When you are asking for a suggestion on a topic which is pretty open, give more examples. I mean to say, if you just give one example and explain what are you targeting doesn't give enough information. For example, if you have sentences which following specific patterns, then it becomes easier to extract information (in your desired format) from them. Otherwise, it becomes broad and complex research problem!

From your example, it looks like you want to extract the head words of a sentence and other words which modify those heads. You can use dependency parsing for this task. Look at Stanford Neural Network Dependency Parser. A dependency parser analyzes the grammatical structure of a sentence, establishing relationships between "head" words and words which modify those heads. So, i believe it should help you in your desired task.

If you want to make it more general, then this problem aligns well with Open Information Extraction. You may consider looking into Stanford OpenIE api.

You may also consider Stanford Relation Extractor api in your task. But i strongly believe relation extraction through dependency parsing best suits your problem definition. You can read this paper to get some idea and utilize them in your task.