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note: we are currently improving documentation and fixing known env bugs - please be patient :)

Boosting NER

HORUS is meta and multi-level framework designed to provide a set of features at word-level to boost natural language frameworks. It's architecure is based on image processing and text classification clustering algorithms and shows to be helpful especially to noisy data, such as microblogs.

We are currently investigating Named Entity Recognition (NER) as use case. This version supports the identification of classical named-entity types (LOC, PER, ORG).

Setup (Python 2.7 based)

  1. Make sure you have Java installed
  • test if this runs:
java -XX:ParallelGCThreads=2 -Xmx2048m -jar /**X**/horus-ner/data/models/tweetnlp/ark-tweet-nlp-0.3.2.jar --model /**X**/horus-ner/data/models/tweetnlp/model.ritter_ptb_alldata_fixed.20130723

X = local path to the code

  1. install CMake, Boost, Boost.Python, and X11

    Ubuntu

    • sudo apt-get install build-essential cmake
    • sudo apt-get install libgtk-3-dev
    • sudo apt-get install libboost-all-dev

    OSX

  2. Setup the python environment

    • setup Anaconda
    • conda env create -f horus.v015.yml (tested for OSX) (*)
    • source activate horus_27_cv310

  3. run ./prerequisites_setup.sh

  4. Setup the framework environment

(*) - setup openCV 3.1: OSx users can benefit from anaconda, which provides a running version of OpenCV 3.1.0 (see more at #issue 6)

Demo

python main.py --text="whitney houston has been honored in nyc"

python main.py --file="sentences.txt" --ds_format=0

python main.py --file="ritter_ner.tsv" --ds_format=1 --output_file="metadata" --output_format="json"

Features

1. to process an input file (e.g., conll) and generate the horus feature file format
    - examples/process_input_file.py

2. to convert a horus input file format to conll
    - horus/core/data_conversion.py

3. to run some benchmarks
    - experiments/benchmarking.py

Web Service

export FLASK_APP=rest.py
user$ flask run

browser:
http://localhost:5000/annotate?text=paris hilton

pip (soon :-))

pip install horus

import horus
config = HorusConfig()
sentence = "paris hilton was once the toast of the town"
extractor = FeatureExtraction(config, load_sift=1, load_tfidf=1, load_cnn=0, load_topic_modeling=1)
print(extractor.extract_features_text(sentence))

Changelog

  • 0.1.0 initial version
  • 0.1.1 adding text classification
  • 0.1.2 adding map detection
  • 0.1.5
    • text-classification enhancements
    • bug-fix
  • 0.2.0
    • web service
    • topic modeling