Pengenalan entitas bernama pada teks transkripsi

Pemrosesan Bahasa Lisan dengan Python

Daniel Bourke

Machine Learning Engineer/YouTube Creator

Menginstal spaCy

# Instal spaCy
$ pip install spacy

# Unduh model bahasa spaCy
$ python -m spacy download en_core_web_sm

Menggunakan spaCy

import spacy


# Muat model bahasa spaCy
nlp = spacy.load("en_core_web_sm")

# Buat doc spaCy
doc = nlp("I'd like to talk about a smartphone I ordered on July 31st from your 
Sydney store, my order number is 40939440. I spoke to Georgia about it last week.")

Token spaCy

# Tampilkan token dan posisinya
for token in doc:
  print(token.text, token.idx)

I 0
'd 1
like 4
to 9
talk 12
about 17
a 23
smartphone 25...

Kalimat spaCy

# Tampilkan kalimat dalam doc
for sentences in doc.sents:
  print(sentence)

I'd like to talk about a smartphone I ordered on July 31st from your Sydney store, 
my order number is 4093829.
I spoke to one of your customer service team, Georgia, yesterday.

Entitas bernama spaCy

Beberapa entitas bernama bawaan spaCy:

PERSON Orang, termasuk fiksi.
ORG Perusahaan, lembaga, institusi, dll.
GPE Negara, kota, negara bagian.
PRODUCT Objek, kendaraan, makanan, dll. (Bukan layanan.)
DATE Tanggal/periode absolut atau relatif.
TIME Waktu kurang dari satu hari.
MONEY Nilai uang, termasuk satuan.
CARDINAL Angka yang tidak masuk tipe lain.

Entitas bernama spaCy

# Temukan entitas bernama dalam doc
for entity in doc.ents:
  print(entity.text, entity.label_)

July 31st DATE
Sydney GPE
4093829 CARDINAL
one CARDINAL
Georgia GPE
yesterday DATE

Entitas kustom

# Impor kelas EntityRuler
from spacy.pipeline import EntityRuler

# Periksa pipeline spaCy
print(nlp.pipeline)

[('tagger', <spacy.pipeline.pipes.Tagger at 0x1c3aa8a470>),
 ('parser', <spacy.pipeline.pipes.DependencyParser at 0x1c3bb60588>),
 ('ner', <spacy.pipeline.pipes.EntityRecognizer at 0x1c3bb605e8>)]

Mengubah pipeline

# Buat instance EntityRuler
ruler = EntityRuler(nlp)

# Tambahkan pola token ke ruler
ruler.add_patterns([{"label":"PRODUCT", "pattern": "smartphone"}])

# Tambahkan aturan baru ke pipeline sebelum ner
nlp.add_pipe(ruler, before="ner")

# Periksa pipeline yang diperbarui
nlp.pipeline

Mengubah pipeline

[('tagger', <spacy.pipeline.pipes.Tagger at 0x1c1f9c9b38>),
 ('parser', <spacy.pipeline.pipes.DependencyParser at 0x1c3c9cba08>),
 ('entity_ruler', <spacy.pipeline.entityruler.EntityRuler at 0x1c1d834b70>),
 ('ner', <spacy.pipeline.pipes.EntityRecognizer at 0x1c3c9cba68>)]

Menguji pipeline baru

# Uji aturan entitas baru
for entity in doc.ents:
    print(entity.text, entity.label_)

smartphone PRODUCT
July 31st DATE
Sydney GPE
4093829 CARDINAL
one CARDINAL
Georgia GPE
yesterday DATE

Ayo berlatih spaCy!

Pemrosesan Bahasa Lisan dengan Python