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README.rst

polyglot

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Polyglot is a natural language pipeline that supports massive multilingual applications.

Features

  • Tokenization (165 Languages)
  • Language detection (196 Languages)
  • Named Entity Recognition (40 Languages)
  • Part of Speech Tagging (16 Languages)
  • Sentiment Analysis (136 Languages)
  • Word Embeddings (137 Languages)
  • Morphological analysis (135 Languages)
  • Transliteration (69 Languages)

Developer

  • Rami Al-Rfou @ rmyeid gmail com

Quick Tutorial

import polyglot
from polyglot.text import Text, Word

Language Detection

text = Text("Bonjour, Mesdames.")
print("Language Detected: Code={}, Name={}\n".format(text.language.code, text.language.name))
Language Detected: Code=fr, Name=French

Tokenization

zen = Text("Beautiful is better than ugly. "
           "Explicit is better than implicit. "
           "Simple is better than complex.")
print(zen.words)
[u'Beautiful', u'is', u'better', u'than', u'ugly', u'.', u'Explicit', u'is', u'better', u'than', u'implicit', u'.', u'Simple', u'is', u'better', u'than', u'complex', u'.']
print(zen.sentences)
[Sentence("Beautiful is better than ugly."), Sentence("Explicit is better than implicit."), Sentence("Simple is better than complex.")]

Part of Speech Tagging

text = Text(u"O primeiro uso de desobediência civil em massa ocorreu em setembro de 1906.")

print("{:<16}{}".format("Word", "POS Tag")+"\n"+"-"*30)
for word, tag in text.pos_tags:
    print(u"{:<16}{:>2}".format(word, tag))
Word            POS Tag
------------------------------
O               DET
primeiro        ADJ
uso             NOUN
de              ADP
desobediência   NOUN
civil           ADJ
em              ADP
massa           NOUN
ocorreu         ADJ
em              ADP
setembro        NOUN
de              ADP
1906            NUM
.               PUNCT

Named Entity Recognition

text = Text(u"In Großbritannien war Gandhi mit dem westlichen Lebensstil vertraut geworden")
print(text.entities)
[I-LOC([u'Gro\xdfbritannien']), I-PER([u'Gandhi'])]

Polarity

print("{:<16}{}".format("Word", "Polarity")+"\n"+"-"*30)
for w in zen.words[:6]:
    print("{:<16}{:>2}".format(w, w.polarity))
Word            Polarity
------------------------------
Beautiful        0
is               0
better           1
than             0
ugly            -1
.                0

Embeddings

word = Word("Obama", language="en")
print("Neighbors (Synonms) of {}".format(word)+"\n"+"-"*30)
for w in word.neighbors:
    print("{:<16}".format(w))
print("\n\nThe first 10 dimensions out the {} dimensions\n".format(word.vector.shape[0]))
print(word.vector[:10])
Neighbors (Synonms) of Obama
------------------------------
Bush
Reagan
Clinton
Ahmadinejad
Nixon
Karzai
McCain
Biden
Huckabee
Lula


The first 10 dimensions out the 256 dimensions

[-2.57382345  1.52175975  0.51070285  1.08678675 -0.74386948 -1.18616164
  2.92784619 -0.25694436 -1.40958667 -2.39675403]

Morphology

word = Text("Preprocessing is an essential step.").words[0]
print(word.morphemes)
[u'Pre', u'process', u'ing']

Transliteration

from polyglot.transliteration import Transliterator
transliterator = Transliterator(source_lang="en", target_lang="ru")
print(transliterator.transliterate(u"preprocessing"))
препрокессинг
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