OpenNLP_Examples

• The Apache OpenNLP library is a machine learning based toolkit for processing of natural language text. It includes a sentence detector, a tokenizer, a name finder, a parts-of-speech (POS) tagger, a chunker, and a parser.

  In this Repository, I will show you how to use Apache OpenNLP through a set of simple examples.

1. Sentence Detector
2. Tokenizer
3. Name Finder
4. POS Tagger
5. Chunker
6. Parser

Sentence Detector

Sentence detector is for detecting sentence boundaries.
The trained openNLP model "en-sent.bin" used for this.

Tokenizer

Tokens are usually words which are separated by space, but there are exceptions. 
For example, "isn't" gets split into "is" and "n't, since it is a a brief format of "is not".
The trained openNLP model "en-token.bin" used for this operation. 

Name Finder

By its name, name finder just finds names in the context.
It accepts an array of strings, and find the names inside, So need to tokenize the input data first.

POS Tagger

Part-of-speech tagging is the process of marking up a word in a text (corpus) as corresponding
to a particular part of speech such as noun, verb, adjective, etc.


POS Tags and their meanings:-

CC: Coordinating conjuction                             PRP$: Prossessive pronoun
CD: Cardinal number                                     RB: Adverb
DT: Determiner                                          RBR: Adverb, Comparative
EX: Existential there                                   RBS: Adverb, Superlative
FW: Foreign word                                        RP: Particle
IN: preposition or subordinating conjunction            SYM: Symbol
JJ: Adjective                                           TO: to
JJR: Adjective, Comparative                             UH: Interjection
JJS: Adjective, Superlative                             VB:Verb, base form
LS: List item marker                                    VBD: Verb, Past tense
MD: Modal                                               VBG: Verb, gerund or past participle
NN: Noun, singular or mass                              VBN:Verb, past participle
NNS: NOun, plural                                       VBP: Verb, non3rd person singular present
NNP: Proper noun, singular                              VBZ: Verb, 3rd person singular present
NNPS: Proper noun, plural                               WDT: Whdeterminer
PDT: Predeterminer                                      WP: Whpronoun
POS: Possessive ending                                  WP$: Possessive whpronoun
PRP: Personal pronoun                                   WRB: Wh adverb

Chunker

What chunker does is to partition a sentence to a set of chunks by using the tokens generated by tokenizer.

Parser

Parser is used for create a parse tree with POS tags.

NOTES : All the required trained models are included in this repo, so only need to provide proper path to the files.

Happy Coding :)