Novel Word Features for Keyword Extraction

Chen, Yiqun; Yin, Jian; Zhu, Weiheng; Qiu, Shiding

doi:10.1007/978-3-319-21042-1_12

Cited by 5 publications

(3 citation statements)

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“…Gain( f GPS , S) = Entropy(S) init − Entropy(S| f GPS ) = 0.97 − 0.55 = 0.42 (19) For each extracted keyword, we can repeat the Formulas (5)- (7) to calculate IG for the classification system. In our experimental dataset, each category consists of 500 patent documents, hence our dataset has a discrete uniform distribution.…”

Section: Comparison Results and Analysismentioning

confidence: 99%

“…A classifier determines whether each word or phrase in the document is a keyword. Many commonly used classification algorithms have been tried, such as decision trees [23], Naive Bayes classifiers [24], Support Vector Machines (SVM) [19], maximum entropy models [25], hidden Markov models [26], conditional random field models [14], and so on. Witten et al [24] proposed a simple and efficient key phrase extraction algorithm (KEA) based on the Naive Bayes algorithm.…”

Section: Pkea (Our Approach)mentioning

confidence: 99%

“…Based on the usage of extracted keywords, keyword extraction algorithms can be roughly divided into two categories: one type are algorithms for extracting semantic keywords to summarize corresponding text [17,19] and the other type are for extracting discriminative keywords to classify texts into categories [16]. Both tasks require that the extracted keywords can represent the document well.…”

Section: Introductionmentioning

confidence: 99%

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Patent Keyword Extraction Algorithm Based on Distributed Representation for Patent Classification

Yao

et al. 2018

Entropy

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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.

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Section: Comparison Results and Analysismentioning

confidence: 99%