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58 lines
2.2 KiB
Python
58 lines
2.2 KiB
Python
5 years ago
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from __future__ import print_function
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from __future__ import unicode_literals
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from builtins import str, bytes, dict, int
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import os
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import sys
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
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from pattern.en import parse, pprint, tag
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# The en module contains a fast regular expressions-based parser.
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# A parser identifies words in a sentence, word part-of-speech tags (e.g. noun, verb)
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# and groups of words that belong together (e.g. noun phrases).
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# Common part-of-speech tags: NN (noun), VB (verb), JJ (adjective), PP (preposition).
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# A tag can have a suffix, for example NNS (plural noun) or VBG (gerund verb).
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# Overview of tags: http://www.clips.ua.ac.be/pages/mbsp-tags
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s = "I eat pizza with a fork."
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s = parse(s,
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tokenize = True, # Tokenize the input, i.e. split punctuation from words.
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tags = True, # Find part-of-speech tags.
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chunks = True, # Find chunk tags, e.g. "the black cat" = NP = noun phrase.
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relations = True, # Find relations between chunks.
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lemmata = True, # Find word lemmata.
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light = False)
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# The light parameter determines how unknown words are handled.
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# By default, unknown words are tagged NN and then improved with a set of rules.
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# light=False uses Brill's lexical and contextual rules,
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# light=True uses a set of custom rules that is less accurate but faster (5x-10x).
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# The output is a string with each sentence on a new line.
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# Words in a sentence have been annotated with tags,
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# for example: fork/NN/I-NP/I-PNP
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# NN = noun, NP = part of a noun phrase, PNP = part of a prepositional phrase.
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print(s)
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print("")
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# Prettier output can be obtained with the pprint() command:
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pprint(s)
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print("")
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# The string's split() method will (unless a split character is given),
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# split into a list of sentences, where each sentence is a list of words
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# and each word is a list with the word + its tags.
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print(s.split())
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print("")
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# The tag() command returns a list of (word, POS-tag)-tuples.
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# With light=True, this is the fastest and simplest way to get an idea
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# of a sentence's constituents:
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s = "I eat pizza with a fork."
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s = tag(s)
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print(s)
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for word, tag in s:
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if tag == "NN": # Find all nouns in the input string.
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print(word)
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