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KöKyPhiRiRieYo

Abstract:

This paper reports the results of a study on automatic keyword extraction in German. We employed in gen- eral 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 Tex- tRank 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.

Type: Misc

Year: 2020
Author:Max Kölbl and Yuki Kyogoku and Nathanael Philipp and Michael Richter and Clemens Rietdorf and Tariq Yousef
Title:Keyword extraction in German: Information-theory vs. deep learning
@MISC{KöKyPhiRiRieYo,
YEAR = {2020},
AUTHOR = {Max Kölbl and Yuki Kyogoku and Nathanael Philipp and Michael Richter and Clemens Rietdorf and Tariq Yousef},
TITLE = {Keyword extraction in German: Information-theory vs. deep learning }
}