For syntctic Features either you can use NON terminal productions with PCFG (Vetribi parser) or you can use Stanford parser, these are dependency parsers

Note: stanford parser takes time to parse each sentence. If u have list of sentences use raw_parse_sents instead raw_parse

You can also look at pattern.en and spacy packages, the parsers from these packages are quite good and fast but it depends on your need.

I suggest you to refer the book http://www.allitebooks.in/text-analytics-python/

chapter 3

Regards

Udaysimha Nerella

Thank you so much for your answer Udaysimha. I want to know whether am I in the right path. Can you suggest me any other (simple, scalable and reliable) method with which I can achieve the same?

https://github.com/python/mypy/wiki/Semantic-Analyzer

Good luck.

Go to Matlab helpfile

https://gerardnico.com/lang/python/grammar

https://jakevdp.github.io/WhirlwindTourOfPython/03-semantics-variables.html

For Syntax: https://rosettacode.org/wiki/Compiler/syntax_analyzer

and Semantic: https://github.com/python/mypy/wiki/Semantic-Analyzer

will be helpful.

Help file provide you more information

the python nltk module is build based on the two functions (syntax and semantics). my study was the beginning at one of block forms in natural language processing is called "named entity recognition." The idea is to let the machine immediately pull out "entities" such as time, people, places, things, locations, and so on. Then you can apply with the logic learning to analysis the document in the meaning of semantics.

this is my understanding, hope it helps. ^_^

Absolutely right