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.

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  • 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 (+) vector-based, adds vectors to measure multi-word terms; (-) non-incremental vocabulary, long pre-processing times PMI (+) large vocab, because it uses any search engine (like Google); (-) cannot measure relatedness between whole sentences or documents SOC-PMI (+) sort lists of important neighbor words from a large corpus; (-) 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 Degrees 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. Demo is available here. 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
  • Einer Theorie nach kann die normalisierte Google-Distanz (engl. normalized Google distance, kurz NGD) als statistische Größe für die semantische Nähe zweier Begriffe oder semantischer Konzepte dienen. Sie wird über die Anzahl der Treffer ermittelt, die für zwei in die Suchmaschine Google eingegebene Begriffe gefunden werden, sprich die Anzahl der Dokumente, welche beide Begriffe enthalten. Die NGD liegt normalerweise zwischen 0 und 1, je geringer sie ist, desto enger hängen zwei Begriffe zusammen.
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  • 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.
  • Einer Theorie nach kann die normalisierte Google-Distanz (engl. normalized Google distance, kurz NGD) als statistische Größe für die semantische Nähe zweier Begriffe oder semantischer Konzepte dienen. Sie wird über die Anzahl der Treffer ermittelt, die für zwei in die Suchmaschine Google eingegebene Begriffe gefunden werden, sprich die Anzahl der Dokumente, welche beide Begriffe enthalten.
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  • Semantic relatedness
  • Normalisierte Google-Distanz
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