Phrase-Based Document Categorization

Koster, C. H. A.; Beney, Jean; Verberne, Suzan; Vogel, Merijn

doi:10.1007/978-3-642-19231-9_13

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…Beney (2010) remarks that this improvement may be language-dependent. As a follow-up, Koster et al (2011) investigated the added value of syntactic phrases. They found that attributive phrases, that is, combinations of adjective or nouns with nouns, were by far the most important syntactic phrases for patent classification.…”

Section: Patent Classificationmentioning

confidence: 99%

“…The Naive Bayes classifier yielded a lower accuracy. We therefore only used the Balanced Winnow algorithm for our classification experiments, which were run with the following LCS configuration, based on tuning experiments on the same data by Koster et al (2011): r Global term selection (GTS): Document frequency minimum is 2, term frequency minimum is 3. Although initial term selection is necessary when dealing with such a large corpus, we deliberately aimed at keeping as many of the sparse phrasal terms as possible.…”

Section: Classification Experimentsmentioning

confidence: 99%

“…r Winnow configuration: We performed tuning experiments for the Winnow parameters on a development set of around 100,000 documents. We arrived at using the same setting as Koster et al (2011), namely, α = 1.02, β = 0.98, θ+ = 2.0, θ− = 0.5, with a maximum of 10 training iterations.…”

Section: Classification Experimentsmentioning

confidence: 99%

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Text Representations for Patent Classification

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With the increasing rate of patent application filings, automated patent classification is of rising economic importance. This article investigates how patent classification can be improved by using different representations of the patent documents. Using the Linguistic Classification System (LCS), we compare the impact of adding statistical phrases (in the form of bigrams) and linguistic phrases (in two different dependency formats) to the standard bag-of-words text representation on a subset of 532,264 English abstracts from the CLEF-IP 2010 corpus.In contrast to previous findings on classification with phrases in the Reuters-21578 data set, for patent classification the addition of phrases results in significant improvements over the unigram baseline. The best results were achieved by combining all four representations, and the second best by combining unigrams and lemmatized bigrams. This article includes extensive analyses of the class models (a.k.a. class profiles) created by the classifiers in the LCS framework, to examine which types of phrases are most informative for patent classification. It appears that bigrams contribute most to improvements in classification accuracy. Similar experiments were performed on subsets of French and German abstracts to investigate the generalizability of these findings.

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Section: Patent Classificationmentioning

confidence: 99%

Section: Classification Experimentsmentioning

confidence: 99%