2
$\begingroup$

I am building an NLP model which uses MEMM in order to tag parts of speech.

My model uses history of two previous words and tags, and of the next word, alongside the current word and tag. I used those values and created basics features (f100-f107, capitals letters and digits).

I am trying to figure more advanced binary features. When I tested the model, it confuses mainly between JJ and NN. How can I think about features that could help my model detecting those POS and not confuse and switch between them?

Improve this question
$\endgroup$

1 Answer 1

Reset to default
2
$\begingroup$

JJ is short for adjective and NN stands for noun. Those can be easily confused because you are using a windowing method. Another option would be use a dependency parser to construct a parse tree which provides more information about the structure of language to the model.

Improve this answer
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.