Stanford CoreNLP provides a set of human language technology tools. It can give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and syntactic dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract particular or open-class relations between entity mentions, get the quotes people said, etc.
A dependency parser analyzes the grammatical structure of a sentence, establishing relationships between "head" words and words which modify those heads.
OpenNLP is an organizational center for open source projects related to natural language processing. It hosts a variety of java-based NLP tools which perform sentence detection, tokenization, pos-tagging, chunking and parsing, named-entity detection, and coreference using the OpenNLP Maxent machine learning package.
Features
* (Jointly) visualize
o syntactic dependency graphs
o semantic dependency graphs (a la CoNLL 2008)
o Chunks (such as syntactic chunks, NER chunks, SRL chunks etc.)
* Compare gold standard trees to your generated trees (e.g. highlight false positive and negative dependency edges)
* Filter trees and visualize only what's necessary, for example
o only dependency edges with certain labels
o only the edges between certain tokens
* Search corpora for sentences with certain attributes using powerful search expressions, for example
o search for all sentences that contain the word "vantage" and the pos tag sequence DT NN
o search for all sentences that contain false positive edges and the word "vantage"
* Reads
o CoNLL 2000, 2002, 2003, 2004, 2006 and 2008 format
o Lisp S-Expressions
o Malt-Tab format
o markov thebeast format
* Export to EPS
Check this screenshot to get a better idea.