Patent Keyword Extraction Algorithm Based on Distributed Representation for Patent Classification

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

Keyword extraction and headline generation using novel word features

We introduce several novel word features for keyword extraction and headline generation. These new word features are derived according to the background knowledge of a document as supplied by Wikipedia. Given a document, to acquire its background knowledge from Wikipedia, we first generat e a query for searching the Wikipedia corpus based on the key facts present in the document. We then use the query to find articles in the Wikipedia corpus that are closely related to the contents of the document. With the Wikipedia search result article set, we extract the inlink, outlink, category and infobox information in each article to derive a set of novel word features which reflect the document's background knowledge. These newly introduced word features of fer valuable indications on individual words' importance in the input document. They serve as nice complements to the traditional word features derivable from explicit information of a document. In addition, we also introduce a word-document fitness feat ure to characterize the influence of a document's genre on the keyword extraction and headline generation process. We study the effectiveness of these novel word features for keyword extraction and headline generation by experiments and have obtained very encouraging results. Copyright © 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.link_to_subscribed_fulltex