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over time. We used Latent Semantic Analysis (LSA), Topic Model (TM) and Co-citation Analysis (CCA), which are popular methods applied in computational semantics and in bibliometrics for analysing scientific papers extracted from bibliographic

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In this study, we examine and validate the use of existing text mining techniques (based on the vector space model and latent semantic indexing) to detect similarities between patent documents and scientific publications. Clearly, experts involved in domain studies would benefit from techniques that allow similarity to be detected—and hence facilitate mapping, categorization and classification efforts. In addition, given current debates on the relevance and appropriateness of academic patenting, the ability to assess content-relatedness between sets of documents—in this case, patents and publications—might become relevant and useful. We list several options available to arrive at content based similarity measures. Different options of a vector space model and latent semantic indexing approach have been selected and applied to the publications and patents of a sample of academic inventors (n = 6). We also validated the outcomes by using independently obtained validation scores of human raters. While we conclude that text mining techniques can be valuable for detecting similarities between patents and publications, our findings also indicate that the various options available to arrive at similarity measures vary considerably in terms of accuracy: some generally accepted text mining options, like dimensionality reduction and LSA, do not yield the best results when working with smaller document sets. Implications and directions for further research are discussed.

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modeling a child’s semantic memory. In: Thomas K. Landauer— Danielle S. McNamara — Simon Dennis— Walter Kintsch (eds): Handbook of latent semantic analysis, 143–165. Lawrence Erlbaum, Mahwah NJ. Lemaire B

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. Harshman. 1990. Indexing by latent semantic analysis. J. Amer. Soc. Inform. Sci. 41: 391-407. Indexing by latent semantic analysis J. Amer. Soc. Inform. Sci

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Thought . Basic Books, New York, NY Landauer , T. K., Dumais , S. T. (1997) A solution to Plato’s problem: the latent semantic analysis theory of acquisition, induction, and representation of knowledge

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high dimensionality and inability in modeling some natural language features (synonymy for instance). The most well-known method in this family is Latent Semantic Analysis (LSA) [ 15 ]. Despite their wide use, deterministic methods are criticized for

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dissent variable names (Sepkoski 1974 ; Batagelj and Bren 1995 ). 6 Corpora-based similarity calculation (e.g. Point-wise Mutual Information and Latent Semantic Analysis) have already been applied for the calculation

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–document similarity was measured on the basis of textual content, however, and partitions of the datasets were not generated. It was observed that SO outperformed both FO and approaches derived from latent semantic analysis with respect to a nearest neighbour test

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Song , X 2010 Exploring the feasibility and accuracy of latent semantic analysis based text mining techniques to detect similarity between patent documents and scientific publications . Scientometrics 82 2 289 – 306 10.1007/s

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Authors: Thomas Gurney, Edwin Horlings, and Peter van den Besselaar

—Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA)—linking authors to words and topics, and then using a clustering method—hierarchical agglomerative clustering—to disambiguate names. The testing was conducted using web data and

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