Abstract
Many text mining tasks such as text retrieval, text summarization, and text comparisons depend on the extraction of representative keywords from the main text. Most existing keyword extraction algorithms are based on discrete bag-of-words type of word representation of the text. In this paper, we propose a patent keyword extraction algorithm (PKEA) based on the distributed Skip-gram model for patent classification. We also develop a set of quantitative performance measures for keyword extraction evaluation based on information gain and cross-validation, based on Support Vector Machine (SVM) classification, which are valuable when human-annotated keywords are not available. We used a standard benchmark dataset and a homemade patent dataset to evaluate the performance of PKEA. Our patent dataset includes 2500 patents from five distinct technological fields related to autonomous cars (GPS systems, lidar systems, object recognition systems, radar systems, and vehicle control systems). We compared our method with Frequency, Term Frequency-Inverse Document Frequency (TF-IDF), TextRank and Rapid Automatic Keyword Extraction (RAKE). The experimental results show that our proposed algorithm provides a promising way to extract keywords from patent texts for patent classification.
Highlights
Patents are an important part of intellectual property
The reliability and performance of subsequent analyses will be affected, which in turn makes it hard to draw reliable insights from analysis results. Considering these issues, this paper examines the effectiveness of deep learning-based keyword extraction methods and proposes a keyword extraction method based on the Skip-gram [20,21,22] model to effectively extract keywords from patent text for patent classification
We develop a method to extract representative keywords from patents, which are used as the features of the patent text for high performance classification by Support Vector Machine (SVM) classifiers
Summary
Patents are an important part of intellectual property. Effective patent analysis may bring lots of benefits for the enterprise. Usually automated patent classifiers are applied to a huge number of patent applications, which are inspected by patent examiner to check the proof for the classification to make final classification decision. This is especially true for classification predictions that have low confidence by the classifiers. Due to this special requirement, high-performance patent classifiers that can explain their classification with extracted keywords, ready for quick inspection by the patent examiner, are strongly desirable
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