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I am learning about NLP, with an eye to starting some practical NLP projects. I see that many of the algorithms for relation extraction and named entity recognition require you to identify linguistic features like "word comes before a period" or "word is the last word in a verb phrase." This seems like it takes a lot of manual work. You have to look at instances in the text and parse out features.

I also know that in distant supervised relation extraction they generate tons of features from tons of examples -- so it seems like there are ways to extract features automatically. Are there any ways of doing this? For instance, are there ways to grep for known names and then automatically detect the features around those names? Can anyone list the names of some methods that exist for doing this?

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closed as too broad by Raphael May 4 '14 at 10:12

Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.

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    $\begingroup$ I guess it's clear that the answer is "yes". Surveys of whole subfields are not suitable for SE, though; please do some research yourself (you should be able to list some methods that exist) and formulate more focussed questions about these methods. Roaming Linguistics might provide you with some important buzzwords. $\endgroup$ – Raphael May 4 '14 at 10:12
  • $\begingroup$ @Raphael could you point me towards certain papers or algorithms? I am not sure where to begin finding these methods. $\endgroup$ – bernie2436 May 4 '14 at 13:51
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    $\begingroup$ en.wikipedia.org/wiki/Latent_semantic_analysis $\endgroup$ – bernie2436 May 8 '14 at 2:19