sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
from pattern.search import search, taxonomy, Classifier
from pattern.en import parsetree
# The search module includes a Taxonomy class
# that can be used to define semantic word types.
# For example, consider that you want to extract flower names from a text.
# This would make search patterns somewhat unwieldy:
# search("rose|lily|daisy|daffodil|begonia", txt).
# A better approach is to use the taxonomy:
for flower in ("rose", "lily", "daisy", "daffodil", "begonia"):
taxonomy.append(flower, type="flower")
print taxonomy.children("flower")
print taxonomy.parents("rose")
print taxonomy.classify("rose") # Yields the most recently added parent.
print
# Taxonomy terms can be included in a pattern by using uppercase:
t = parsetree("A field of white daffodils.", lemmata=True)
m = search("FLOWER", t)
print t
print m
print
# Another example:
taxonomy.append("chicken", type="food")
taxonomy.append("chicken", type="bird")
taxonomy.append("penguin", type="bird")
from pattern.search import search, taxonomy
for f in ('rose', 'lily', 'daisy', 'daffodil', 'begonia'):
taxonomy.append(f, type='flower')
for f in ('flower', 'tree'):
taxonomy.append(f, type='plant')
t = parsetree('A field of daffodils is white.', lemmata=True)
print search('PLANT', t)
taxonomy.parents('daffodil', recursive=True)
taxonomy.children('plant', recursive=False)
#def taxonomy_normalize(sentence):
# bp_match = search('BEAUTY_PARTS', parsetree(sentence, lemmata=True))
# facial_match = search('MAKEUP', parsetree(sentence, lemmata=True))
# feet_match = search('FEET', parsetree(sentence, lemmata=True))
# body_match = search('BODY', parsetree(sentence, lemmata=True))
#
# matches = [ [ 'BEAUTY_PARTS-'+word.lemma for word in m] for m in bp_match ] \
# + [ [ 'MAKEUP-'+word.lemma for word in m] for m in facial_match ] \
# + [ [ 'FEET-'+word.lemma for word in m] for m in feet_match ] \
# + [ [ 'BODY-'+word.lemma for word in m] for m in body_match ]
#
# return matches
import os, sys; sys.path.append(os.path.join("..", "..", ".."))
from pattern.search import Pattern, Constraint, Classifier, taxonomy
from pattern.en import Sentence, parse
# The search module includes a Taxonomy class
# that can be used to define semantic word types.
# For example, consider that you want to extract flower names from a text.
# This would make patterns somewhat unwieldy, e.g.:
# Pattern.fromstring("rose|lily|daisy|daffodil|begonia").
# A better approach is to use the taxonomy:
for flower in ("rose", "lily", "daisy", "daffodil", "begonia"):
taxonomy.append(flower, type="flower")
print taxonomy.children("flower")
print taxonomy.parents("rose")
print taxonomy.classify("rose") # Yields the most recently added parent.
print
# Taxonomy terms can be included in a pattern:
p = Pattern([Constraint(taxa=["flower"])]) # or
p = Pattern.fromstring("FLOWER")
s = Sentence(parse("A field of white daffodils.", lemmata=True))
m = p.search(s)
print s
print m
print
from pattern.search import search
for f in ('reflect', 'bank'):
taxonomy.append(f, type='angle')
for f in ('bank', 'financial-institution'):
taxonomy.append(f, type='finance')
t = parsetree('A field of daffodils is white.', lemmata=True)
print search('PLANT', t)
taxonomy.parents('daffodil', recursive=True)
taxonomy.children('plant', recursive=False)
taxonomy.classify('bank')
from pattern.en import wordnet
a = wordnet.synsets('tone')[4]
b = wordnet.synsets('color')[0]
wordnet.similarity(a,b)
