Abstract
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 strengths 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
Highlights
1 Introduction geometric distribution such as lexical specificity (Lafon, 1980) can perform at least as well as or Keyword extraction has been an essential task in many scientific fields as a first step to extract relevant terms from text corpora
Different variants have been proposed, which we summarize in two main alternatives: tf-idf (Section 2.1.1) and lexical specificity (Section 2.1.2)
For mean reciprocal rank (MRR), LexSpec exhibits a very different behaviour, performing significantly better in datasets with high average number of noun phrases and high variability in the number of gold keywords
Summary
1 Introduction geometric distribution such as lexical specificity (Lafon, 1980) can perform at least as well as or Keyword extraction has been an essential task in many scientific fields as a first step to extract relevant terms from text corpora. Graph-based methods initialized with tf-idf or lexical specificity performs best overall. Pablos and García-Peñalvo, 2020) and Natural Lan- ment. As each of such phrases consists of conguage Processing (NLP) tasks (Riedel et al, 2017; tiguous words in the document, the task can be Arroyo-Fernández et al, 2019). As an extension of tf, term frequency–inverse document frequency (tf-idf) (Jones, 1972) is one of most popular and effective methods used for statistical keyword extraction (El-Beltagy and Rafea, 2009), as well as still being an important component in modern information retrieval applications (Marcos-Pablos and García-Peñalvo, 2020; Guu et al, 2020).
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