poyrazb/ner4opt

Ner4Opt: Named-Entity Recognition for Optimization

Ner4Opt: Named Entity Recognition for Optimization

Given an optimization problem in natural language, Ner4Opt extracts optimization related entities from free-form text.

See our HugginFace Space Demo to test it yourself 🤗.

The Ner4Opt model (CPAIOR'23, NeurIPS'22) is trained to detect six named entities:

• CONST_DIR: Constraint direction
• LIMIT: Limit
• OBJ_DIR: Objective direction
• OBJ_NAME: Objective name
• PARAM: Parameter
• VAR: Variable

Here are the details of our pre-trained models and the training procedure. Large pretrained models are hosted on HuggingFace Models.

Quick Start

# Import
from ner4opt import Ner4Opt

# Input optimization problem description as free-form text
problem_description = "Cautious Asset Investment has a total of $150,000 to manage and decides to invest it in money market fund, which yields a 2% return as well as in foreign bonds, which gives and average rate of return of 10.2%. Internal policies require PAI to diversify the asset allocation so that the minimum investment in money market fund is 40% of the total investment. Due to the risk of default of foreign countries, no more than 40% of the total investment should be allocated to foreign bonds. How much should the Cautious Asset Investment allocate in each asset so as to maximize its average return?"

# Ner4Opt Model with options lexical, lexical_plus, semantic, hybrid (default). 
ner4opt = Ner4Opt(model="hybrid")

# Extract a list of dictionaries corresponding to entities found in the given problem description.
# Each dictionary holds keys for the following: 
# start (starting character index of the entity), end (ending character index of the entity)
# word (entity), entity_group (entity label) and score (confidence score for the entity)
entities = ner4opt.get_entities(problem_description)

# Output
print("Number of entities found: ", len(entities))

# Example entity output
[   
    {
        'start': 32, 
        'end': 37, 
        'word': 'total', 
        'entity_group': 'CONST_DIR', 
        'score': 0.997172257043559
    },
    {
        'start': 575, 
        'end': 583, 
        'word': 'maximize', 
        'entity_group': 'OBJ_DIR', 
        'score': 0.9982091561140413
    },
    { ... },
]

Installation

Ner4Opt requires Python 3.8+ and can be installed from PyPI using pip install ner4opt or by building from source

git clone https://github.com/skadio/ner4opt.git
cd ner4opt
pip install .

Testing

To run tests, execute the following from the root folder:

python -m unittest

Citation

Citation

If you use Ner4Opt, please cite the following paper:

@inproceedings {ner4opt,
    title = {Ner4Opt: Named Entity Recognition for Optimization Modelling from Natural Language}
	author = {Parag Pravin Dakle, Serdar Kadıoğlu, Karthik Uppuluri, Regina Politi, Preethi Raghavan, SaiKrishna Rallabandi, Ravisutha Srinivasamurthy}
    journal = {The 20th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR 2023)},
	year = {2023},
}