ca-address-parser

This is a minimal implementation of a Canada address parser built using spaCy NLP library. This blog post covers the implementation and execution details at length.

Prerequisites

• Python v3.x
• spaCy v3.x

Folders

• config
• corpus
  • dataset
  • rules
  • spacy-docbins
• output
  • models
  • model-best
  • model-last

Training corpus

A sample corpus of Canadaian addresses to train/test the parser is present under corpus/dataset folder. JSON based rules required by Entity ruler are present under [corpus/rules](<>)

Config

config contains files for initializing training parameters:

base_config.cfg: Initializes pipeline and training batch size. base_config_er.cfg: Similar as base_config but with additional entity ruler settings. config.cfg: Pre filled config file obtained after executing inti fill-config. config_er.cfg: Pre filled config file with additional entity ruler settings.

Output

output contains final trained models (with and without entity rules)

Training prerequisites

Before starting the training process, we need to:

i) Obtain a pre-filled training config which has the required training parameters.

ii) Build spacy-docbin (binary serialized representation) files for training and test dataset.

Pre-filled training config: Below command can be executed from command-line to get a pre filled config file. This would take as input the base_config.cfg file and churn out the pre-filled training config file: config.cfg.

python -m spacy init fill-config config/base_config.cfg config/config.cfg

Similarly, to get entity-ruler based config, pointing this command to the base_config_er.cfg would churn out the pre filled config : config_er.cfg

Prepare spacy-docbins: Finally, a spacy-docbin file can be obtained by executing ca_training_data_prep.py.

python ca_training_data_prep.py

This would take raw csv training/test datasets as inputs and churn out docbin files under corpus/spacy-docbins folder.

Training loop execution

To start the training process, below train command can be executed:

python -m spacy train config/config.cfg –paths.train corpus/spacy-docbins/train.spacy –paths.dev corpus/spacy-docbins/test.spacy –output output/models –training.eval_frequency 10 –training.max_steps 300

This saves the output NER models under output folder.

Predictions

Predictions for a few sample Canadian addresses can be checked by executing ca_predict.py

python ca_predict.py

Output

Address string -> 16, 15 SILVER SPRINGS WY NW, AIRDRIE
Parsed address -> [('16', 'ADDR_UNIT'), ('15', 'ADDR_STREET_NUMBER'), ('SILVER SPRINGS', 'ADDR_STREET_NAME'), ('WY', 'ADDR_STREET_TYPE'), ('NW', 'ADDR_STREET_DIRECTION'), ('AIRDRIE', 'ADDR_CITY')]
******
Address string -> 46, 1008 WOODSIDE WY NW, AIRDRIE
Parsed address -> [('46', 'ADDR_UNIT'), ('1008', 'ADDR_STREET_NUMBER'), ('WOODSIDE', 'ADDR_STREET_NAME'), ('WY', 'ADDR_STREET_TYPE'), ('NW', 'ADDR_STREET_DIRECTION'), ('AIRDRIE', 'ADDR_CITY')]
******
Address string -> 9, 209 WOODSIDE DR NW, AIRDRIE
Parsed address -> [('9', 'ADDR_UNIT'), ('209', 'ADDR_STREET_NUMBER'), ('WOODSIDE', 'ADDR_STREET_NAME'), ('DR', 'ADDR_STREET_TYPE'), ('NW', 'ADDR_STREET_DIRECTION'), ('AIRDRIE', 'ADDR_CITY')]
******
Address string -> 42, 4 STONEGATE DR NW, AIRDRIE
Parsed address -> [('42', 'ADDR_UNIT'), ('4', 'ADDR_STREET_NUMBER'), ('STONEGATE', 'ADDR_STREET_NAME'), ('DR', 'ADDR_STREET_TYPE'), ('NW', 'ADDR_STREET_DIRECTION'), ('AIRDRIE', 'ADDR_CITY')]
******
Address string -> 7, 209 WOODSIDE DR NW, AIRDRIE
Parsed address -> [('7', 'ADDR_UNIT'), ('209', 'ADDR_STREET_NUMBER'), ('WOODSIDE', 'ADDR_STREET_NAME'), ('DR', 'ADDR_STREET_TYPE'), ('NW', 'ADDR_STREET_DIRECTION'), ('AIRDRIE', 'ADDR_CITY')]
******
Address string -> 306, 190 KANANASKIS WAY, CANMORE
Parsed address -> [('306', 'ADDR_UNIT'), ('190', 'ADDR_STREET_NUMBER'), ('KANANASKIS', 'ADDR_STREET_NAME'), ('WAY', 'ADDR_UNIT'), ('CANMORE', 'ADDR_STREET_NUMBER')]

Download more data

https://www.statcan.gc.ca/en/lode/databases/oda