NTCIR-7 Patent Mining Experiments at RALI

Abstract We participated in the patent mining task at NTCIR7 workshop. Particularly, our experiments focus on English corpus. Based on the Indri search engine, we implemented a patent classification system, which is able to assign a research paper into the IPC system according to the annotated patents in the database. As the task is a cross-genre classification task, we tried several methods to bridge the gap between the research papers and patents. Unfortunately, most the methods do not produce consistent improvements. 1. Introduction We participated in the patent mining task at the NTCIR-7 workshop. In this task, we are given the abstracts of a set of research papers, and a large set of patents. Each patent has been labeled with an IPC (International Patent Classification) code. The IPC system can be considered as a classification specification, with each code corresponding to one class. Participants are required to assign an IPC code to each research paper. Therefore, the task is actually a text classification task, in which the abstracts of the research papers are test data and the labeled patents are training data. However, the task is different from the traditional text classification in the following aspects. Firstly, the terms used the research paper are different from those used in the patent. To widen the coverage of a claim, the author of a patent usually prefers to use more general and abstract terms to describe one concept. On the other hand, the research paper aims to describe a concept in a precise and concise way. It results in different styles of the terms used in patents and research papers. For example, a research paper and a patent may use “Apple ipod” and “music player” to describe the same thing. Therefore, there is a gap between the research papers and patents. As we mentioned, in this task, the research paper and patents are test and training data respectively. However, most traditional text classification approaches make one implicit assumption that the term of texts belonging to the same class should have similar distributions; no matter they are training or test data. Obviously, this assumption does not hold in the task. In fact, the text classification task we are investigating here is called cross-genre text classification (Nanba et al., 2008), i.e., the training and test data are heterogeneous. Secondly, the classification system (IPC) of this task is much more complicated than the traditional text classification. The IPC system is a global standard hierarchical patent classification system. The sixth edition of IPC has more than 50,000 classes at the most detailed level. In this task, each patent in the training data has been assigned to one or more classes. Every research paper is required to be classified to an appropriate class according its connection with the patents in the training data. Considering the traditional text classification tasks usually have less than 100 classes (Joachims, 1998), the current task is much more difficult. If we use some parametric classifiers, such as SVM, Naive Bayesian classifier, there would be too many parameters to be computational tractable. Thirdly, the number of training examples (i.e., patents) in the classes is very unbalanced. We show the statistics of the IPC code in table 1. In this table, column one is the range of the number of the patents of an IPC code, and the second column is the number of this kind of IPC codes. Therefore, there are 25944 IPC codes has less than 11 patents. From this table, we can observe that the IPC codes are very unbalanced, i.e., more than half IPC codes have very few patents. For such a biased training data, it is very challenging to train a precise and robust classifier.