Abstract
A skip-bigram is a bigram that allows skips between words. In this paper, we use a set of skip bigrams (a SBGSet) to represent a short word sequence, which is the typical form of a product feature. The advantage of SBGSet representation for word sequences is that we can convert between a sequence and a set. Under the SBGSet representation we can employ association rule mining to find frequent itemsets from which frequent product features can be extracted.For infrequent product features, we use a pattern-based method to extract them. A pattern is also represented by a SBGSet, and contains a variable that can be instantiated to a product feature.We use two data sets to evaluate our method. The experimental result shows that our method is suitable for extracting Chinese product features, and the pattern-based method to extract infrequent product features is effective.
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Xu, G., Huang, CR., Wang, H. (2013). Extracting Chinese Product Features: Representing a Sequence by a Set of Skip-Bigrams. In: Ji, D., Xiao, G. (eds) Chinese Lexical Semantics. CLSW 2012. Lecture Notes in Computer Science(), vol 7717. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36337-5_9
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DOI: https://doi.org/10.1007/978-3-642-36337-5_9
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