Hi all, I am a engineering graduate working on an NLP related project.I am new to Natural language processing and finally understood something about it. My aim of project is to design a lab for various activities for each grammar topics, examples here :
*Grammar topics examples :* subject verb agreement tense verb forms reported speech etc *Activities for tenses topic examples.* Fill in the blank with correct tense : I am ______(to play) football Change the tense of a sentence Recognize the tense of the sentence etc.... I will elaborate 1 activity :*Recognize tense of a sentence .* *Objective of the activity :* We will give some sentences to student and they will identify tense of the sentence in this activity. *Our current procedure* We will come to know tense of sentence using NLP at backend. We have a corpus of English textbooks that we willl use for similar grammar topics. For now I know there are two approaches to this.Rule based NLP and Statistical NLP. So,I can write down rules to identify data that is related to specific activity or use statistical nlp . what should I choose? I know for now there exist various NLP api like Stanford NLP,Opennlp etc. They have models for POS tagging , chunking etc.. *So do I need to make model for each grammar topic if i use statistical approach?* I wonder *how can i make a model* for tense or any other topic and get the data which i require for actiivites. Does that model integrate with other NLP like Stanford etc.... *Is there any other approach?Please tell me if I am going wrong somewhere.*
