Event extraction pipeline.
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README.md

EventMiner

Using the hammer of supervised learning to make events.

About

EventMiner aims to serve, primarily, as an interface to various NLP analytics to extract event information from text. This project is setup with a REST frontend interface, which accepts text input, that is then further passed via a RabbitMQ messaging queue to various analytics as appropriate. The project is comprised of Docker containers, with orchestration handled by docker-compose. This, combined with RabbitMQ as the messaging layer, allows for clean definitions of interactions between services and minimal setup for the end user.

Services

The services defined in this project are as follows (in the order they process content):

  1. mitie - Provide NER tagging via MITIE.
  2. predpatt - Extract predicate-argument structures using PredPatt. Also includes Universal Dependency parse provided by SyntaxNet/DRAGNN.
  3. relevancy - An SVM classifier to determine story relevancy based on the story title.
  • Note: Branching occurs at the relevancy classifier. If this model determines a story is not relevant, it isn't processed by quad, it just goes straight through to hypnos.
  1. quad - A convolutional neural net to classify a sentence into one of four QuadCategories: verbal conflict, verbal cooperation, material conflict, material cooperation.
  2. hypnos - Rule-based event extractor. Used primarily for actor extraction in this setup.
  3. collector - Light process to pull in events and write them out to a file.

Deployment

There are two docker-compose projects that make up mjolnir. The first is the miner application itself. The second is hypnos, which is the container architecture around the PETRARCH2 event extractor. docker-compose must be run for miner first, and hypnos second, due to nuances relating to the shared docker networks. Thus, deployment is as follows (assuming the user starts in the top-level EventMiner directory):

docker-compose up -d 
cd ./hynpos
docker-compose up -d

This will lead to a REST interface deployed on port 6000. With the features of docker-compose, it's possible to arbitrarily scale up the various services connected within miner. For example, the quad service is rather slow since it's a neural net running on a CPU. Since each service consumes from a messaging queue, we don't need to worry about things such as load balancing; each service just consumes when it's ready. Given this, to scale the quad service is as simple as (assuming the user is in the root EventMiner directory):

docker-compose scale quad=3

to run three of the quad containers.

Usage

The interface accepts JSON input via REST. As an example:

import json
import requests

headers = {'Content-Type': 'application/json'}

test = {'title': 'Syrian rebels attacked Aleppo.', 'content': 'This is the content. Rebels attacked Aleppo.'}
data = {'data': test}

r = requests.post('http://localhost:6000/EventMiner', data=json.dumps(data), headers=headers)

The response object from EventMiner will contain a unique ID for the input data that allows the user to trace the progress of the content throughout the pipeline. The pipeline will write data out to the EventMiner/data directory. The results are in a file titled events.YYYYMMDD.txt with one JSON record per line. The output format (for now...) is as follows:

{u'content': u'This is the content. Rebels attacked Aleppo.',
 u'event_info': {u'267bbae4-dcc0-4224-94e9-67679b0b6ad1': {u'coded': [],
   u'predicted_class': {u'class': 4, u'score': u'0.89923'},
   u'sent': u'This is the content.'},
  u'8b464457-18d2-419c-b5c1-49c6131be947': {u'coded': [[u'---REB',
     u'SYR',
     u'190']],
   u'predicted_class': {u'class': 4, u'score': u'0.986187'},
   u'sent': u'Rebels attacked Aleppo.'}},
 u'pipeline_key': u'4c4f7e7a-db31-4137-a888-2cdbbbf1c225',
 u'predicted_relevancy': 1,
 u'sents': {u'267bbae4-dcc0-4224-94e9-67679b0b6ad1': u'This is the content.',
  u'8b464457-18d2-419c-b5c1-49c6131be947': u'Rebels attacked Aleppo.'},
 u'title': u'Syrian rebels attacked Aleppo.'}

Acknowledgements

This work was funded by the DARPA Quantitative Crisis Response (QCR) program.