This git repository documents the code base used in a custom argument retrieval system. The whole documentation can be found in the paper as quoted underneath.

Relying on the background of document retrieval systems, this paper presents an argument retrieval system. It uses state of the art python libraries to automatically process a data set of arguments from the args.me corpus and store them in a custom index. The system can perform the process of ranking and ordering a list of relevant documents in response to a user's query by comparing the request to the produced index of documents. The special focus is on the use of several NLP-based analyses, including topic signal modeling, stylometric analysis, and primarily bias detection. These methods will be used to influence the ranking of the search engine.

EN: This work was realized as part of the course "Information Retrieval" (summer semester 2020) under the supervision of Jun.-Prof. Dr. Martin Potthast, Lukas Gienapp and Christopher Akiki at the University of Leipzig.

DE: Diese Arbeit wurde im Rahmen des Kurses "Information Retrieval" (Sommersemester 2020) unter der Leitung von Jun.-Prof. Dr. Martin Potthast, Lukas Gienapp und Christopher Akiki an der Universität Leipzig realisiert.

git clone https://github.com/Nicolas-le/argumentRetrieval.git

cd argumentRetrieval

pip3 install -r requirements.txt

python3 main.py --i <source> --o <output-file>

source: Link to directory, has to be structured in the same way as the args.me corpus [https://webis.de/data/args-me-corpus.html]

output-file: Link to directory with run.txt file existing in it.

example: python3 main.py --i ./code_base/corpus --o ./code_base/run

requirements: Python 3.6 is necessary at the moment, because of the Pattern.en module and some unfixed issues with this package and the newer python versions.

Elastic Search has to be running as a service before starting the retrieval process. If it's not starting automatically use this command:

sudo systemctl start elasticsearch.service