Authors:
Max Kölbl
;
Yuki Kyogoku
;
J. Nathanael Philipp
;
Michael Richter
;
Clemens Rietdorf
and
Tariq Yousef
Affiliation:
Institute of Computer Science, NLP Group, Universität Leipzig, Augustusplatz 10, 04109 Leipzig, Germany
Keyword(s):
Keyword Extraction, Information Theory, Topic Model, Recurrent Neural Network.
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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