On the Usability of Random Indexing in Patent Retrieval

  • Mihai LupuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8577)


Statistical semantics methods are fairly controversial in the IR community, mostly because of their instability and difficulty to debug. At the same time, they are extremely tempting, in the same way perhaps, as Artificial Intelligence was in the 60s. Then, it took a few decades for the hype to pass and for us to learn the real utility and limits of the great technologies developed earlier. This paper takes an exhaustive view of the performance and utility of a particular statistical semantics method, Random Indexing, in the context of difficult texts. After over a year of CPU time in experiments, we provide a global view of the behaviour of the method on a particularly challenging test collection based on patent data. In the end, we observe interesting patterns emerging in the semantic space created by the method, which we hypothesize to be the cause of the behaviour observed in the experiments.


Latent Semantic Analysis Document Frequency Semantic Space Patent Data Test Collection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Institute of Software Technology and Interactive SystemsVienna University of TechnologyWienAustria

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