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Text Representations for Patent Classification
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DOI：10.1162/COLI_a_00149.png)
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摘要
En 中文
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.
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
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IF:
5.3
论文数:
837
被引数:
2.7K
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