payment-text-parser 0.0.9

Parser for entity/address free text (based on libpostal/spacy)

Payment text parser

Description

Inputs a text string and parses entity/address free text input to:

• Flag entity fields ('ORG' for companies, 'PER' for individuals, 'PER_ORG' for uncertain decision) <= Based on Spacy
• Flag address components of the address ('house number', 'town', 'country', ...) <= Based on Libpostal
• Flag other fields (i.e. not entity nor address) with POS tags ('NE', 'ADJ', 'NN', ..) <= Based on CoreNLP

More generally, the package includes following features:

• A data generator for entity/address fields and free text fields (based on open data)
• A model distinguishing entity/address field from free text (using Keras/TensorFlow and CoreNLP) to apply dedicated heuristics
• A series of cleaning_postprocessing steps including true case recognition (CoreNLP)
• A parser of entity/address/other fields as described above using re-trained vanilla Spacy model (based on labeled open data)
• Simple heuristics and metrics applied after the parsing to improve accuracy
• Part-of-speech (POS) tagging of the remaining flags (Spacy and/or CoreNLP) for downstream processing

This package is specifically intended to be used together with the upstream Swiftflow pipeline that parses all fields from the SWIFT MT messages, including the entity/address and free text fields, which are decisive for inter-banking transactional communication.

Installation

The package uses essentally Libpostal and Spacy. Also, it uses Keras on Tensorflow to recognize if the text input is a free text or an entity/address text.

Pre-requiste: Libpostal

Refer to Libpostal installation. Once Libpostal is installed, the Python binder postal will be installed as part of the package with pip (see below)

Payment_text_parser

The other dependencies, including Spacy, will be installed via pip on the present package:

Create environment

One recommends to use Python 3.7.

Native Python:

/usr/local/bin/python3 -m venv <my_env>
source <my_env>/bin/activate`

Conda:

conda create --name <my_env> python=3.7` conda activate <my_env>

From pip

pip install payment-text-parser --use-feature=2020-resolver
python -m spacy download de_core_news_sm

From git

pip install git+https://gitlab.com/alpina-analytics/payment_text_parser.git
python -m spacy download de_core_news_sm

From requirements.txt

git clone https://gitlab.com/alpina-analytics/payment_text_parser.git
cd payment_text_parser
pip install -r requirements.txt
python -m spacy download de_core_news_sm
export PYTHONPATH=$(pwd)

Usage

Script

from payment_text_parser.entity_extractor.entity_extractor import ExtractorClass
e = ExtractorClass(text)
d_res = e.d_res

Webserver

# Launch
python main.py

# Test
curl -H "Content-type: application/json" -X POST http://127.0.0.1:5000/parse -d '{"text":"John Deere Les Abues 2 75000 Paris"}'

Optional : start Stanford NLP server

Required if :

• Field type detection enabled by ExtractorClass(text,check_field_type=True)
• POS-tagging of rest fields enabled by ExtractorClass(text,create_nlp_tags_rest_text=True) If not started, an warning message will be prompted, however full processing can still take place.

CoreNLP server can be started as follow:

cd ./core_nlp/stanford-corenlp-full-2018-10-05
java -Xmx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer \
-serverProperties StanfordCoreNLP-german.properties \
-preload tokenize,ssplit,pos,parse \
-status_port 9000  -port 9000 -timeout 15000

References

Spacy

https://spacy.io/

Libpostal

https://github.com/openvenues/libpostal

CoreNLP

https://stackoverflow.com/questions/33259191/installing-libicu-dev-on-mac https://stackoverflow.com/questions/50217214/import-error-for-icu-in-mac-and-ubuntu-although-pyicu-is-installed-correctly/50364835#50364835 https://www.khalidalnajjar.com/setup-use-stanford-corenlp-server-python/