Semantic relatedness
This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "Semantic_relatedness".

Main article: Semantic similarity

Computational Measures of Semantic Relatedness are publicly available means for approximating the relative meaning of words/documents. These have been used for essay-grading by the Educational Testing Service, search engine technology, predicting which links people are likely to click on, etc.

  • LSA (Latent semantic analysis) (+) vector-based, adds vectors to measure multi-word terms; (-) non-incremental vocabulary, long pre-processing times
  • PMI (Pointwise Mutual Information) (+) large vocab, because it uses any search engine (like Google); (-) cannot measure relatedness between whole sentences or documents
  • GLSA (Generalized Latent Semantic Analysis) (+) vector-based, adds vectors to measure multi-word terms; (-) non-incremental vocabulary, long pre-processing times
  • ICAN (Incremental Construction of an Associative Network) (+) incremental, network-based measure, good for spreading activation, accounts for second-order relatedness; (-) cannot measure relatedness between multi-word terms, long pre-processing times
  • NGD (Normalized Google Distance; see below) (+) large vocab, because it uses any search engine (like Google); (-) cannot measure relatedness between whole sentences or documents
  • WordNet: (+) humanly constructed; (-) humanly constructed (not automatically learned), cannot measure relatedness between multi-word term, non-incremental vocabulary
  • ESA (Explicit Semantic Analysis) based on Wikipedia and the ODP
  • n° of Wikipedia (noW), inspired by the game Six Degree of Wikipedia, is a distance metric based on the hierarchical structure of Wikipedia. A directed-acyclic graph is first constructed and later, Dijkstra's shortest path algorithm is employed to determine the noW value between two terms as the geodesic distance between the corresponding topics (i.e. nodes) in the graph.
  • VGEM (Vector Generation of an Explicitly-defined Multidimensional Semantic Space) (+) incremental vocab, can compare multi-word terms (-) performance depends on choosing specific dimensions
  • BLOSSOM (Best path Length On a Semantic Self-Organizing Map) (+) uses a Self Organizing Map to reduce high dimensional spaces, can use different vector representations (VGEM or word-document matrix), provides 'concept path linking' from one word to another (-) highly experimental, requires nontrivial SOM calculation
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Contents

Semantic similarity measures

SimRank

Main article: SimRank

Google distance

Google distance is a measure of semantic interrelatedness derived from the number of hits returned by the Google search engine for a given set of keywords. Keywords with the same or similar meanings in a natural language sense tend to be "close" in units of Google distance, while words with dissimilar meanings tend to be farther apart.

Specifically, the normalized Google distance between two search terms x and y is

\operatorname{NGD}(x,y) = \frac{\max\{\log f(x), \log f(y)\} - \log f(x,y)} {\log M - \min\{\log f(x), \log f(y)\}}

where M is the total number of web pages searched by Google; f(x) and f(y) are the number of hits for search terms x and y, respectively; and f(x, y) is the number of web pages on which both x and y occur.

If the two search terms x and y never occur together on the same web page, but do occur separately, the normalized Google distance between them is infinite. If both terms always occur together, their NGD is zero.

See also

References

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  • Dumais, S. (2003). Data-driven approaches to information access. Cognitive Science, 27(3), 491-524.
  • Gabrilovich, E. and Markovitch, S. (2007). "Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis", Proceedings of The 20th International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India, January 2007. [1]
  • Juvina, I., van Oostendorp, H., Karbor, P., & Pauw, B. (2005). Towards modeling contextual information in web navigation. In B. G. Bara & L. Barsalou & M. Bucciarelli (Eds.), 27th Annual Meeting of the Cognitive Science Society, CogSci2005 (pp. 1078-1083). Austin, Tx: The Cognitive Science Society, Inc.
  • Kaur, I. & Hornof, A.J. (2005). A Comparison of LSA, WordNet and PMI for Predicting User Click Behavior. Proceedings of the Conference on Human Factors in Computing, CHI 2005 (pp. 51-60).
  • 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. Psychological Review, 104(2), 211-240.
  • Landauer, T. K., Foltz, P. W., & Laham, D. (1998). Introduction to Latent Semantic Analysis. Discourse Processes, 25, 259-284.
  • Lee, M. D., Pincombe, B., & Welsh, M. (2005). An empirical evaluation of models of text document similarity. In B. G. Bara & L. Barsalou & M. Bucciarelli (Eds.), 27th Annual Meeting of the Cognitive Science Society, CogSci2005 (pp. 1254-1259). Austin, Tx: The Cognitive Science Society, Inc.
  • Lemaire, B., & Denhiére, G. (2004). Incremental construction of an associative network from a corpus. In K. D. Forbus & D. Gentner & T. Regier (Eds.), 26th Annual Meeting of the Cognitive Science Society, CogSci2004. Hillsdale, NJ: Lawrence Erlbaum Publisher.
  • Lindsey, R., Veksler, V.D., Grintsvayg, A., Gray, W.D. (2007). The Effects of Corpus Selection on Measuring Semantic Relatedness. Proceedings of the 8th International Conference on Cognitive Modeling, Ann Arbor, MI.
  • Pirolli, P. (2005). Rational analyses of information foraging on the Web. Cognitive Science, 29(3), 343-373.
  • Pirolli, P., & Fu, W.-T. (2003). SNIF-ACT: A model of information foraging on the World Wide Web. Lecture Notes in Computer Science, 2702, 45-54.
  • Turney, P. (2001). Mining the Web for Synonyms: PMI versus LSA on TOEFL. In L. De Raedt & P. Flach (Eds.), Proceedings of the Twelfth European Conference on Machine Learning (ECML-2001) (pp. 491-502). Freiburg, Germany.
  • Veksler, V.D. & Gray, W.D. (2006). Test Case Selection for Evaluating Measures of Semantic Distance. Proceedings of the 28th Annual Meeting of the Cognitive Science Society, CogSci2006.
  • Wong, W., Liu, W. & Bennamoun, M. (2008) Featureless Data Clustering. In: M. Song and Y. Wu; Handbook of Research on Text and Web Mining Technologies; IGI Global. [isbn: 978-1-59904-990-8] (the use of NGD and noW for term and URI clustering)

Google distance references

External links
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