Pencocokan berbasis aturan

NLP Lanjutan dengan spaCy

Ines Montani

spaCy core developer

Mengapa tidak pakai regular expression?

Cocok pada objek Doc, bukan hanya string
Cocok pada token dan atribut token
Gunakan prediksi model
Contoh: "duck" (verba) vs. "duck" (nomina)

Pola kecocokan

Daftar dictionary, satu per token
Cocok teks token persis
```
[{'ORTH': 'iPhone'}, {'ORTH': 'X'}]
```
Cocok atribut leksikal
```
[{'LOWER': 'iphone'}, {'LOWER': 'x'}]
```
Cocok atribut token apa pun
```
[{'LEMMA': 'buy'}, {'POS': 'NOUN'}]
```

Menggunakan Matcher (1)

import spacy

# Import the Matcher
from spacy.matcher import Matcher

# Load a model and create the nlp object
nlp = spacy.load('en_core_web_sm')

# Initialize the matcher with the shared vocab
matcher = Matcher(nlp.vocab)

# Add the pattern to the matcher
pattern = [{'ORTH': 'iPhone'}, {'ORTH': 'X'}]
matcher.add('IPHONE_PATTERN', None, pattern)

# Process some text
doc = nlp("New iPhone X release date leaked")

# Call the matcher on the doc
matches = matcher(doc)

Menggunakan Matcher (2)

# Call the matcher on the doc
doc = nlp("New iPhone X release date leaked")
matches = matcher(doc)

# Iterate over the matches
for match_id, start, end in matches:

    # Get the matched span
    matched_span = doc[start:end]
    print(matched_span.text)

iPhone X

match_id: nilai hash nama pola
start: indeks awal span yang cocok
end: indeks akhir span yang cocok

Mencocokkan atribut leksikal

pattern = [
    {'IS_DIGIT': True},
    {'LOWER': 'fifa'},
    {'LOWER': 'world'},
    {'LOWER': 'cup'},
    {'IS_PUNCT': True}
]

doc = nlp("2018 FIFA World Cup: France won!")

2018 FIFA World Cup:

Mencocokkan atribut token lain

pattern = [
    {'LEMMA': 'love', 'POS': 'VERB'},
    {'POS': 'NOUN'}
]

doc = nlp("I loved dogs but now I love cats more.")

loved dogs
love cats

Menggunakan operator dan kuantifier (1)

pattern = [
    {'LEMMA': 'buy'},
    {'POS': 'DET', 'OP': '?'},  # opsional: cocok 0 atau 1 kali
    {'POS': 'NOUN'}
]

doc = nlp("I bought a smartphone. Now I'm buying apps.")

bought a smartphone
buying apps

Menggunakan operator dan kuantifier (2)

	Deskripsi
`{'OP': '!'}`	Negasi: cocok 0 kali
`{'OP': '?'}`	Opsional: cocok 0 atau 1 kali
`{'OP': '+'}`	Cocok ≥1 kali
`{'OP': '*'}`	Cocok 0 atau lebih kali

Ayo berlatih!

NLP Lanjutan dengan spaCy