A Study Using $n$-gram Features for Text Categorization

By Johannes Fürnkranz. 1998

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Citations in the corpus

Text Categorization as a Graph Classification Problem (2015, background): “Because its size is exponential in the number of edges (just like the number of n-grams is exponential in n), it is common to only retain/mine the most frequent subgraphs (again just like for n-grams with a minimum document frequency (Fürnkranz, 1998; Joachims, 1998)).”
Fake News Detection on Social Media: A Data Mining Perspective (2017, background): “equency of large words, and unique words; (ii) syntactic features, including sentence-level features, such as frequency of function words and phrases (i.e., \n-grams" and bag-of-words approaches [24]) or punctuation and partsof-speech (POS) tagging. Domain-specic linguistic features, which are ”
The use of bigrams to enhance text categorization (2002, result): “Fürnkranz (1998) came to a similar conclusion.”
Stop Clickbait: Detecting and preventing clickbaits in online news media (2016, methodology): “In order to limit the number of N-gram features used based on their frequency of occurrence, we pruned the feature space efficiently by using the sub-sequence property and an APRIORI-like algorithm [14].”
Graph-based term weighting for text categorization (2015, methodology): “Then, we briefly describe the classification models that are used in the core of a TC system.”
The repetition rate of text as a predictor of the effectiveness of machine translation adaptation (2014, background): “…and statistical phrases ( n -grams) have been investigated for a long time and many works report classiﬁcation improvements over the use of single tokens, especially by introducing not too long n -grams (F¨urnkranz, 1998), outcome that we exploit by deﬁning the repetition rate over 1-to 4-grams.”
Keyword extraction from a single document using word co-occurrence statistical information (2004, methodology): “) and extract phrases based on the APRIORI algorithm (Fürnkranz 1998).”