Research team focused on argument-based recommender systems. We are part of the Information Retrieval Group (IRG) at UAM.
ArgRecSys Team @ UAM
Research team focused on argument-based recommender systems. We are part of the Information Retrieval Group (IRG) at UAM.
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- Madrid, Spain
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- arg-min-fw Public
Welcome to the Framework for Mining Arguments repository! This project aims to provide a comprehensive framework for mining arguments from textual data.
argrecsys/arg-min-fw’s past year of commit activity - arg-miner Public
This repository contains a simple but efficient implementation of an argument-based recommender system, which makes use of NLP techniques and a taxonomy and lexicon of connectors to extract argument graphs from the proposals and citizen debates available in the Decide Madrid e-participation platform.
argrecsys/arg-miner’s past year of commit activity - arg-classifier Public
Implementation of a traditional classifier of argumentative components (claims and premises), trained with features/metadata previously extracted from manually annotated argumentative sentences from the citizen proposals available in the Decide Madrid platform.
argrecsys/arg-classifier’s past year of commit activity - arg-ir-tool Public
ArgIR: Tool for annotation and retrieval of argumentative information from textual content. A case study in the Decide Madrid database. The search runs on Apache Lucene and the results (proposals and comments) are re-ranked according to the number of arguments they have.
argrecsys/arg-ir-tool’s past year of commit activity - decide-madrid-2019-annotations Public
Repository with the labels and annotations made on the Decide Madrid 2019 dataset, using different types of annotation tools.
argrecsys/decide-madrid-2019-annotations’s past year of commit activity