How to parse ‘go’Natural Language Processing in Ruby

Tom Cartwright @tomcartwrightuk

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Python, surely? Yes. The NLTK is awesome.

But you have a Ruby-based app.

Extracting meaning from !human input

Summarisation Extracting entities Tagging text Sentiment analysis Filtering text

document sentence word example

From document level!!

!

!

!

to word level

Chunking & segmenting

document sentence word example

Breaking text into paragraphs, sentences and other zones

Start with a document/some text:

“The second nonabsolute number is the given time of arrival, which is now known to be one of those most bizarre of mathematical concepts, a recipriversexclusion, a number whose existence can only be defined as being anything other than itself…..”

document sentence word example

Punkt sentence tokenizer to the rescue….

tokenizer = Punkt::SentenceTokenizer.new(!

"The second nonabsolute number is the given time

of arrival...")!

!

result = !

tokenizer.sentences_from_text(text,!

:output => :sentences_text)!

!!!

document sentence word example

Training

trainer = Punkt::Trainer.new()!trainer.train(bistromatic_text)

document sentence word example

TokenisingBreaking text into words, phrases and symbols.

“Time is an illusion. Lunchtime doubly so.”.split(“ “)!!#=> !![“Time", “is", “an", “illusion.”, “Lunchtime", “doubly", “so.”]!

document sentence word example

class Tokenizer FS = Regexp.new(‘[[:blank:]]+') PAIR_PRE = ['(', '{', '['] SIMPLE_POST = ['!', '?', ',', ':', ';', '.'] PAIR_POST = [')', '}', ']'] PRE_N_POST = ['"', “'"] …

Regexes and rules Tokenizer gem

document sentence word example

tokenizer = Tokenizer::Tokenizer.new tokenizer.tokenize(“Time is an illusion. Lunchtime doubly so.”)

#=>

document sentence word example

[“Time", “is", “an", “illusion", “.”, “Lunchtime", “doubly", “so", “.”]

StemmingJogging => Jog

“jogging”.gsub(/.ing/, “”) !#=> “jog"!!

“bring”.gsub(/.ing/, “”) !#=> “b"

document sentence word example

stemmer = Lingua::Stemmer.new(:language => "en") stemmer.stem("programming") #=> program stemmer.stem("vimming") #=> vim

1. Ruby-Stemmer ⇒ multi-language porter stemmer 2. Text ⇒ porter stemmer

document sentence word example

CC ⇒ conjunction ⇒ and, but DET ⇒ determiner ⇒ this, some IN ⇒ preposition / conjunction ⇒ above, about JJ ⇒ adjective ⇒ orange, tiny NNP ⇒ proper noun ⇒ Camden Pale Ale

Parts-of-speech tagging

document sentence word example

!Regex tagger

/*.ing/ ⇒ VBG /*.ed/ ⇒ VBD

!Lookup on words

E.g. calculating : { VBG: 6 }

orange: { JJ: 2, NN: 5 }

A couple of methods!

document sentence word example

A tale of two taggers

rb-brill-tagger

• Probabilistic (uses look up table prev. slide)

• Brown corpus trained • Pure ruby

• Rule based • C extensions

EngTagger

document sentence word example

Treat gemBundles many of the gems shown

Wraps them in a DSL

stemming; tokenising; chunking; serialising; tagging; text extraction from pdfs and html;

s = sentence(“A really good sentence.”) s.do(:chunk, :segment, :tokenize, :parse)

document sentence word example

LRUG Sentiments

{NN}A tag ⇒

Pass in regex => /({JJ}|{JJS})({NNS}|{NNP})/

And some tagged tokens

#=> [(Word @tag="JJ", @text="jolly"),!(Word @tag="NN", @text="face")]

1.0 ! epic!1.0 good!0.21875 chance!0.21875 brisk!-1.0 slanderous!-1.0 piteous

Sentimental value

Results!!!

• dedicated servers!• pdfs!• Surrey

• Ruby!• Practical Object-

Oriented Design in Ruby!

• Doctors!• Lrug!• recruiters (!)

• unsolicited phone calls from r********s!

• clients!• Paypal!• XML!• geeks

GemsText - Paul Battley’s box of tricks Treat Tokenizer Punkt segmenter Chronic - for extracting dates

Other things you can do/I didn’t talk about

Calculate text edit distance Extract entities using the Stanford libraries via the RJB !Extract topic words (LDA) !Keyword extraction - TfIdf !Jruby

Thank you for processing. Questions?

@tomcartwrightuk

Thanks to Tim Cowlishaw and the HT dev team for specialised rubber duck support