NLP-PoS-Tagging

A file is given as an input and text is then pre-processed, tokenized, stemmed and lemmatized. After this, all the lemmatized words are tagged in context of Brown Corpus. A list of words along with there respective tags is obtained a a result. Some side results are also obtained:

1. Bar-Graphs:
   1. Relationship between word length and frequency (tokens with stopwords)
   2. Relationship between word length and frequency (tokens without stopwords)
   3. Top 10 most occured word in the file (with stopwords)
   4. Top 10 most occured word in the file (without stopwords)
   5. Relationship between word tags and frequency
2. Word Cloud:
   1. Of tokens (with stopwords)
   2. Of tokens (without stopwords)