Term weighting schemes are widely used in Natural Language Processing and Information Retrieval. In particular, term weighting is the basis for keyword extraction. However, there are relatively few evaluation studies that shed light about the strengt
hs and shortcomings of each weighting scheme. In fact, in most cases researchers and practitioners resort to the well-known tf-idf as default, despite the existence of other suitable alternatives, including graph-based models. In this paper, we perform an exhaustive and large-scale empirical comparison of both statistical and graph-based term weighting methods in the context of keyword extraction. Our analysis reveals some interesting findings such as the advantages of the less-known lexical specificity with respect to tf-idf, or the qualitative differences between statistical and graph-based methods. Finally, based on our findings we discuss and devise some suggestions for practitioners. Source code to reproduce our experimental results, including a keyword extraction library, are available in the following repository: https://github.com/asahi417/kex
This research addresses an old and new controversy: the Quranic
context versus occasions of revelations: which of them is stronger
semantically for preference if it proves impossible to reconcile them
within one apparent context. What is old in th
is research is the
importance of context for semantic preference in terms of deciding what
is meant by the Quranic text as appears from the context, and in terms of
clarifying and specifying generalizations, in addition to context being the
meaning that links words and Ayat (verses) together.
