argument-mining-framework
Advanced tools
Argument Mining Framework (AMF) is a comprehensive toolkit designed to streamline and unify various argument mining modules into a single platform.
AMF is a comprehensive toolkit designed to streamline and unify various argument mining modules into a single platform. By leveraging the Argument Interchange Format (AIF), AMF enables seamless communication between different components, including segmenters, turnators, argument relation identifiers, and argument scheme classifiers.
AMF provides a modular approach to argument mining, integrating various components into a cohesive framework. The main features include:
Ensure you have Python installed on your system. AMF is compatible with Python 3.6 and above.
It's recommended to create a virtual environment to manage dependencies:
python -m venv amf-env
Activate the virtual environment:
.\amf-env\Scripts\activate
source amf-env/bin/activate
With the virtual environment activated, install AMF using pip:
pip install argument-mining-framework
This command will install the latest version of AMF along with its dependencies.
The Argument Segmentor component is responsible for detecting and segmenting arguments within text.
The Turnator identifies and segments dialogue turns, facilitating the analysis of conversations and interactions within texts. This module is particularly useful for dialogue-based datasets.
This component identifies and categorizes the relationships between argument units.
The Argument Scheme Classifier categorizes arguments based on predefined schemes, enabling structured argument analysis.
Below is an example of how to use the AMF Predictor class to generate an argument map using an input provided based on AIF:
from argument_mining_framework.argument_relation.predictor import ArgumentRelationPredictor
import json
# Initialize Predictor
predictor = ArgumentRelationPredictor(model_type="dialogpt", variant="vanilla")
# Example XAIF structure
xaif = {
"AIF": {
"nodes": [
{"nodeID": "1", "text": "THANK YOU", "type": "I", "timestamp": "2016-10-31 17:17:34"},
{"nodeID": "2", "text": "COOPER : THANK YOU", "type": "L", "timestamp": "2016-11-10 18:34:23"},
# Add more nodes as needed
],
"edges": [
{"edgeID": "1", "fromID": "1", "toID": "20", "formEdgeID": "None"},
{"edgeID": "2", "fromID": "20", "toID": "3", "formEdgeID": "None"}
# Add more edges as needed
],
"locutions": [],
"participants": []
},
"text": "people feel that they have been treated disrespectfully..."
}
# Convert XAIF structure to JSON string
xaif_json = json.dumps(xaif)
# Predict argument relations
result_map = predictor.argument_map(xaif_json)
print(result_map)
In this section, we demonstrate how to use multiple components of the AMF framework in a complete argument mining workflow. This example shows how to process a text input through the Turninator, Segmenter, Propositionalizer, and Argument Relation Predictor components and visualize the output.
from argument_mining_framework.loader import Module
def process_pipeline(input_data):
"""Process input data through the entire pipeline."""
# Initialize components
turninator = load_amf_component('turninator')()
segmenter = load_amf_component('segmenter')()
propositionalizer = load_amf_component('propositionalizer')()
argument_relation = load_amf_component('argument_relation', "dialogpt", "vanila")
visualiser = load_amf_component('visualiser')()
# Step 1: Turninator
turninator_output = turninator.get_turns(input_data, True)
print(f'Turninator output: {turninator_output}')
# Step 2: Segmenter
segmenter_output = segmenter.get_segments(turninator_output)
print(f'Segmenter output: {segmenter_output}')
# Step 3: Propositionalizer
propositionalizer_output = propositionalizer.get_propositions(segmenter_output)
print(f'Propositionalizer output: {propositionalizer_output}')
# Step 4: Argument Relation Prediction
argument_map_output = argument_relation.get_argument_map(propositionalizer_output)
print(f'Argument relation prediction output: {argument_map_output}')
# Additional Analysis
print("Get all claims:")
print(argument_relation.get_all_claims(argument_map_output))
print("===============================================")
print("Get evidence for claim:")
print(argument_relation.get_evidence_for_claim(
"But this isn’t the time for vaccine nationalism", argument_map_output))
print("===============================================")
print("Visualise the argument map")
visualiser.visualise(argument_map_output)
def main():
# Sample input data
input_data = (
"""Liam Halligan: Vaccines mark a major advance in human achievement since the """
"""enlightenment into the 19th Century and Britain’s been at the forefront of """
"""those achievements over the years and decades. But this isn’t the time for """
"""vaccine nationalism. I agree we should congratulate all the scientists, those """
"""in Belgium, the States, British scientists working in international teams here """
"""in the UK, with AstraZeneca.\n"""
"""Fiona Bruce: What about the logistical capabilities? They are obviously """
"""forefront now, now we’ve got a vaccine that’s been approved. It’s good -- I’m """
"""reassured that the British Army are going to be involved. They’re absolute world """
"""experts at rolling out things, complex logistic capabilities. This is probably """
"""going to be the biggest logistical exercise that our armed forces have undertaken """
"""since the Falklands War, which I’m old enough to remember, just about. So, as a """
"""neutral I’d like to see a lot of cross-party cooperation, and I’m encouraged with """
"""Sarah’s tone, everybody wants to see us getting on with it now. They don’t want """
"""to see competition on whose vaccine is best. There will be some instances where """
"""the Pfizer vaccine works better, another where you can’t have cold refrigeration, """
"""across the developing world as well, a cheaper vaccine like the AstraZeneca works """
"""better. Let’s keep our fingers crossed and hope we make a good job of this."""
)
process_pipeline(input_data)
if __name__ == "__main__":
main()
For detailed API documentation, please refer to the official documentation or check out the source code on GitHub.
We welcome contributions from the community. Please read our contributing guidelines to get started.
The AMF is licensed under the GNU General Public License (GPL) v3.0, with additional custom terms.
For more detailed information about the GPL v3.0 license, visit the GPL License page.
FAQs
Argument Mining Framework (AMF) is a comprehensive toolkit designed to streamline and unify various argument mining modules into a single platform.
We found that argument-mining-framework demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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