Keyword Extraction in German: Information-theory vs. Deep Learning

Max Kölbl, Yuki Kyogoku, J. Philipp, Michael Richter, Clemens Rietdorf, Tariq Yousef

Abstract

This paper reports the results of a study on automatic keyword extraction in German. We employed in general two types of methods: (A) an unsupervised method based on information theory (Shannon, 1948). We employed (i) a bigram model, (ii) a probabilistic parser model (Hale, 2001) and (iii) an innovative model which utilises topics as extra-sentential contexts for the calculation of the information content of the words, and (B) a supervised method employing a recurrent neural network (RNN). As baselines, we employed TextRank and the TF-IDF ranking function. The topic model (A)(iii) outperformed clearly all remaining models, even TextRank and TF-IDF. In contrast, RNN performed poorly. We take the results as first evidence, that (i) information content can be employed for keyword extraction tasks and has thus a clear correspondence to semantics of natural language’s, and (ii) that - as a cognitive principle - the information content of words is determined from extra-sentential contexts, that is to say, from the discourse of words.

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Paper Citation


in Harvard Style

Kölbl M., Kyogoku Y., Philipp J., Richter M., Rietdorf C. and Yousef T. (2020). Keyword Extraction in German: Information-theory vs. Deep Learning.In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: NLPinAI, ISBN 978-989-758-395-7, pages 459-464. DOI: 10.5220/0009374704590464


in Bibtex Style

@conference{nlpinai20,
author={Max Kölbl and Yuki Kyogoku and J. Philipp and Michael Richter and Clemens Rietdorf and Tariq Yousef},
title={Keyword Extraction in German: Information-theory vs. Deep Learning},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: NLPinAI,},
year={2020},
pages={459-464},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009374704590464},
isbn={978-989-758-395-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: NLPinAI,
TI - Keyword Extraction in German: Information-theory vs. Deep Learning
SN - 978-989-758-395-7
AU - Kölbl M.
AU - Kyogoku Y.
AU - Philipp J.
AU - Richter M.
AU - Rietdorf C.
AU - Yousef T.
PY - 2020
SP - 459
EP - 464
DO - 10.5220/0009374704590464