Entities Recognition

Note

This tutorial is available as an IPython notebook here.

%%time
import malaya
CPU times: user 6.58 s, sys: 1.5 s, total: 8.08 s
Wall time: 12.3 s

Describe supported entities

malaya.describe_entities()
OTHER - Other
law - law, regulation, related law documents, documents, etc
location - location, place
organization - organization, company, government, facilities, etc
person - person, group of people, believes, etc
quantity - numbers, quantity
time - date, day, time, etc
event - unique event happened, etc

List available Transformer NER models

malaya.entity.available_transformer_model()
{'bert': ['base', 'small'], 'xlnet': ['base'], 'albert': ['base']}

Make sure you can check accuracy chart from here first before select a model, https://malaya.readthedocs.io/en/latest/Accuracy.html#entities-recognition

You might want to use ALBERT, a very small size, 43MB, but the accuracy is still on the top notch.

string = 'KUALA LUMPUR: Sempena sambutan Aidilfitri minggu depan, Perdana Menteri Tun Dr Mahathir Mohamad dan Menteri Pengangkutan Anthony Loke Siew Fook menitipkan pesanan khas kepada orang ramai yang mahu pulang ke kampung halaman masing-masing. Dalam video pendek terbitan Jabatan Keselamatan Jalan Raya (JKJR) itu, Dr Mahathir menasihati mereka supaya berhenti berehat dan tidur sebentar  sekiranya mengantuk ketika memandu.'

Load ALBERT model

model = malaya.entity.transformer(model = 'albert', size = 'base')
WARNING: Logging before flag parsing goes to stderr.
W1017 22:28:20.427351 4703032768 deprecation_wrapper.py:119] From /Users/huseinzol/Documents/Malaya/malaya/_utils/_utils.py:68: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.

W1017 22:28:20.428478 4703032768 deprecation_wrapper.py:119] From /Users/huseinzol/Documents/Malaya/malaya/_utils/_utils.py:69: The name tf.GraphDef is deprecated. Please use tf.compat.v1.GraphDef instead.

W1017 22:28:21.298430 4703032768 deprecation_wrapper.py:119] From /Users/huseinzol/Documents/Malaya/malaya/_utils/_utils.py:64: The name tf.InteractiveSession is deprecated. Please use tf.compat.v1.InteractiveSession instead.
model.predict(string)
[('Kuala', 'location'),
 ('Lumpur:', 'location'),
 ('Sempena', 'OTHER'),
 ('sambutan', 'OTHER'),
 ('Aidilfitri', 'OTHER'),
 ('minggu', 'OTHER'),
 ('depan,', 'OTHER'),
 ('Perdana', 'person'),
 ('Menteri', 'person'),
 ('Tun', 'person'),
 ('Dr', 'person'),
 ('Mahathir', 'person'),
 ('Mohamad', 'person'),
 ('dan', 'OTHER'),
 ('Menteri', 'person'),
 ('Pengangkutan', 'person'),
 ('Anthony', 'person'),
 ('Loke', 'person'),
 ('Siew', 'person'),
 ('Fook', 'person'),
 ('menitipkan', 'OTHER'),
 ('pesanan', 'OTHER'),
 ('khas', 'OTHER'),
 ('kepada', 'OTHER'),
 ('orang', 'OTHER'),
 ('ramai', 'OTHER'),
 ('yang', 'OTHER'),
 ('mahu', 'OTHER'),
 ('pulang', 'OTHER'),
 ('ke', 'OTHER'),
 ('kampung', 'location'),
 ('halaman', 'location'),
 ('masing-masing.', 'OTHER'),
 ('Dalam', 'OTHER'),
 ('video', 'OTHER'),
 ('pendek', 'OTHER'),
 ('terbitan', 'OTHER'),
 ('Jabatan', 'organization'),
 ('Keselamatan', 'organization'),
 ('Jalan', 'organization'),
 ('Raya', 'organization'),
 ('(JKJR)', 'organization'),
 ('itu,', 'OTHER'),
 ('Dr', 'person'),
 ('Mahathir', 'person'),
 ('menasihati', 'OTHER'),
 ('mereka', 'OTHER'),
 ('supaya', 'OTHER'),
 ('berhenti', 'OTHER'),
 ('berehat', 'OTHER'),
 ('dan', 'OTHER'),
 ('tidur', 'OTHER'),
 ('sebentar', 'OTHER'),
 ('sekiranya', 'OTHER'),
 ('mengantuk', 'OTHER'),
 ('ketika', 'OTHER'),
 ('memandu.', 'OTHER')]
model.analyze(string)
{'words': ['Kuala',
  'Lumpur:',
  'Sempena',
  'sambutan',
  'Aidilfitri',
  'minggu',
  'depan,',
  'Perdana',
  'Menteri',
  'Tun',
  'Dr',
  'Mahathir',
  'Mohamad',
  'dan',
  'Menteri',
  'Pengangkutan',
  'Anthony',
  'Loke',
  'Siew',
  'Fook',
  'menitipkan',
  'pesanan',
  'khas',
  'kepada',
  'orang',
  'ramai',
  'yang',
  'mahu',
  'pulang',
  'ke',
  'kampung',
  'halaman',
  'masing-masing.',
  'Dalam',
  'video',
  'pendek',
  'terbitan',
  'Jabatan',
  'Keselamatan',
  'Jalan',
  'Raya',
  '(JKJR)',
  'itu,',
  'Dr',
  'Mahathir',
  'menasihati',
  'mereka',
  'supaya',
  'berhenti',
  'berehat',
  'dan',
  'tidur',
  'sebentar',
  'sekiranya',
  'mengantuk',
  'ketika',
  'memandu.'],
 'tags': [{'text': 'Kuala Lumpur:',
   'type': 'location',
   'score': 1.0,
   'beginOffset': 0,
   'endOffset': 1},
  {'text': 'Sempena sambutan Aidilfitri minggu depan,',
   'type': 'OTHER',
   'score': 1.0,
   'beginOffset': 2,
   'endOffset': 6},
  {'text': 'Perdana Menteri Tun Dr Mahathir Mohamad',
   'type': 'person',
   'score': 1.0,
   'beginOffset': 7,
   'endOffset': 12},
  {'text': 'dan',
   'type': 'OTHER',
   'score': 1.0,
   'beginOffset': 13,
   'endOffset': 13},
  {'text': 'Menteri Pengangkutan Anthony Loke Siew Fook',
   'type': 'person',
   'score': 1.0,
   'beginOffset': 14,
   'endOffset': 19},
  {'text': 'menitipkan pesanan khas kepada orang ramai yang mahu pulang ke',
   'type': 'OTHER',
   'score': 1.0,
   'beginOffset': 20,
   'endOffset': 29},
  {'text': 'kampung halaman',
   'type': 'location',
   'score': 1.0,
   'beginOffset': 30,
   'endOffset': 31},
  {'text': 'masing-masing. Dalam video pendek terbitan',
   'type': 'OTHER',
   'score': 1.0,
   'beginOffset': 32,
   'endOffset': 36},
  {'text': 'Jabatan Keselamatan Jalan Raya (JKJR)',
   'type': 'organization',
   'score': 1.0,
   'beginOffset': 37,
   'endOffset': 41},
  {'text': 'itu,',
   'type': 'OTHER',
   'score': 1.0,
   'beginOffset': 42,
   'endOffset': 42},
  {'text': 'Dr Mahathir',
   'type': 'person',
   'score': 1.0,
   'beginOffset': 43,
   'endOffset': 44}]}

Load general Malaya entity model

This model able to classify,

  1. date
  2. money
  3. temperature
  4. distance
  5. volume
  6. duration
  7. phone
  8. email
  9. url
  10. time
  11. datetime
  12. local and generic foods, can check available rules in malaya.texts._food
  13. local and generic drinks, can check available rules in malaya.texts._food

We can insert BERT or any deep learning model by passing malaya.entity.general_entity(model = model), as long the model has predict method and return [(string, label), (string, label)]. This is an optional.

entity = malaya.entity.general_entity(model = model)
entity.predict('Husein baca buku Perlembagaan yang berharga 3k ringgit dekat kfc sungai petani minggu lepas, 2 ptg 2 oktober 2019 , suhu 32 celcius, sambil makan ayam goreng dan milo o ais')
{'person': ['Husein'],
 'OTHER': ['baca buku',
  'yang berharga',
  'dekat',
  'lepas, 2 ptg',
  ', suhu 32 celcius, sambil makan ayam goreng dan milo o ais'],
 'law': ['Perlembagaan'],
 'quantity': ['3k ringgit'],
 'location': ['kfc sungai petani'],
 'time': {'2 oktober 2019': datetime.datetime(2019, 10, 2, 0, 0),
  '2 PM': datetime.datetime(2019, 10, 17, 14, 0),
  'minggu': None},
 'date': {'2 oktober 2019': datetime.datetime(2019, 10, 2, 0, 0),
  'minggu lalu': datetime.datetime(2019, 10, 10, 22, 28, 23, 292272)},
 'money': {'3k ringgit': 'RM3000.0'},
 'temperature': ['32 celcius'],
 'distance': [],
 'volume': [],
 'duration': [],
 'phone': [],
 'email': [],
 'url': [],
 'datetime': {'2 ptg 2 oktober 2019': datetime.datetime(2019, 10, 2, 14, 0)},
 'food': ['ayam goreng'],
 'drink': ['milo o ais']}
entity.predict('contact Husein at husein.zol05@gmail.com')
{'OTHER': ['contact Husein at'],
 'person': ['husein.zol05@gmail.com'],
 'date': {},
 'money': {},
 'temperature': [],
 'distance': [],
 'volume': [],
 'duration': [],
 'phone': [],
 'email': ['husein.zol05@gmail.com'],
 'url': [],
 'time': {},
 'datetime': {},
 'food': [],
 'drink': []}
entity.predict('tolong tempahkan meja makan makan nasi dagang dan jus apple, milo tarik esok dekat Restoran Sebulek')
{'OTHER': ['tolong tempahkan meja makan makan nasi',
  'dan',
  'tarik esok dekat Restoran'],
 'person': ['dagang', 'jus apple, milo', 'Sebulek'],
 'date': {'esok': datetime.datetime(2019, 10, 18, 22, 28, 26, 567487)},
 'money': {},
 'temperature': [],
 'distance': [],
 'volume': [],
 'duration': [],
 'phone': [],
 'email': [],
 'url': [],
 'time': {},
 'datetime': {},
 'food': ['nasi dagang'],
 'drink': ['milo tarik', 'jus apple']}

Voting stack model

xlnet = malaya.entity.transformer(model = 'xlnet', size = 'base')
malaya.stack.voting_stack([model, xlnet, xlnet],
'tolong tempahkan meja makan makan nasi dagang dan jus apple, milo tarik esok dekat Restoran Sebulek')
[('tolong', 'OTHER'),
 ('tempahkan', 'OTHER'),
 ('meja', 'OTHER'),
 ('makan', 'OTHER'),
 ('makan', 'OTHER'),
 ('nasi', 'OTHER'),
 ('dagang', 'OTHER'),
 ('dan', 'OTHER'),
 ('jus', 'OTHER'),
 ('apple,', 'OTHER'),
 ('milo', 'person'),
 ('tarik', 'OTHER'),
 ('esok', 'OTHER'),
 ('dekat', 'OTHER'),
 ('Restoran', 'organization'),
 ('Sebulek', 'person')]