BibSonomyburst/user/diego_ma/semantic_closenessBibSonomy RSS feed for /user/diego_ma/semantic_closeness2012-02-17T00:42:14+01:00http://www.bibsonomy.org/bibtex/200ba496f53767b92d5965db71eeea8bf/diego_madiego_ma2008-02-14T07:15:55+01:00semantic_closeness web <span class="authorEditorList"><a href="/author/Cilibrasi">Rudi Cilibrasi</a>, and <a href="/author/Vitanyi">Paul M. B. Vitanyi</a> </span><em>IEEE Transactions on Knowledge and Data Engineering</em> (<em>2007</em>)Thu Feb 14 07:15:55 CET 2008IEEE Transactions on Knowledge and Data Engineering370The Google Similarity Distance192007semantic_closeness web Words and phrases acquire meaning from the way they are used in society, from their relative semantics to other words and phrases. For computers the equivalent of `society' is `database,' and the equivalent of `use' is `way to search the database.' We present a new theory of similarity between words and phrases based on information distance and Kolmogorov complexity. To fix thoughts we use the world-wide-web as database, and Google as search engine. The method is also applicable to other search engines and databases. This theory is then applied to construct a method to automatically extract similarity, the Google similarity distance, of words and phrases from the world-wide-web using Google page counts. The world-wide-web is the largest database on earth, and the context information entered by millions of independent users averages out to provide automatic semantics of useful quality. We give applications in hierarchical clustering, classification, and language translation. We give examples to distinguish between colors and numbers, cluster names of paintings by 17th century Dutch masters and names of books by English novelists, the ability to understand emergencies, and primes, and we demonstrate the ability to do a simple automatic English-Spanish translation. Finally, we use the WordNet database as an objective baseline against which to judge the performance of our method. We conduct a massive randomized trial in binary classification using support vector machines to learn categories based on our Google distance, resulting in an a mean agreement of 87% with the expert crafted WordNet categories.http://www.bibsonomy.org/bibtex/23d2969db6df305b60ee5ce220045c0cc/diego_madiego_ma2007-12-14T02:45:37+01:00WordNet semantic_closeness <span class="authorEditorList"><a href="/author/Resnik">Philip Resnik</a> </span><em>Journal of Artificial Intelligence Research</em> (<em>1998</em>)Fri Dec 14 02:45:37 CET 2007Journal of Artificial Intelligence Research95-130Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language111998WordNet semantic_closeness This article presents a measure of semantic similarity in an IS-A taxonomy based on the notion of shared information content. Experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge-counting approach. The article presents algorithms that take advantage of taxonomic similarity in resolving syntactic and semantic ambiguity, along with experimental results demonstrating their effectiveness.http://www.bibsonomy.org/bibtex/246ca669c1a9508c05bb687945dbc5407/diego_madiego_ma2007-12-14T02:44:10+01:00inf_retrieval semantic_closeness <span class="authorEditorList"><a href="/author/Montes-y-Gómez">Manuel Montes-y-Gómez</a>, <a href="/author/López-López">Aurelio López-López</a>, and <a href="/author/Gelbukh">Alexander Gelbukh</a> </span><em>Proc. DEXA-2000, </em><em> 1873, </em><em>page 312-321. </em><em>Springer-Verlag, </em>(<em>2000</em>)Fri Dec 14 02:44:10 CET 2007Proc. DEXA-20001873312-321Lecture Notes in Computer ScienceInformation Retrieval with Conceptual Graph Matching2000inf_retrieval semantic_closeness The use of conceptual graphs for the representation of text contents in information retrieval is discussed. A method for measuring the similarity between two texts represented as conceptual graphs is presented. The method is based on well-known strategies of text comparison, such as Dice coefficient, with new elements indroduced due to the bipartite nature of the conceptual graphs. Examples of the representation and comparison of the phrases are given. The structure of an information retrieval system using two-level document representation, traditional keywords conceptual graphs, is presented.http://www.bibsonomy.org/bibtex/256fc5f404aea37d8e99b2ae8fa068f58/diego_madiego_ma2007-12-14T02:44:08+01:00inf_retrieval semantic_closeness <span class="authorEditorList"><a href="/author/Montes-y-Gómez">Manuel Montes-y-Gómez</a>, <a href="/author/Gelbukh">Alexander Gelbukh</a>, and <a href="/author/Baeza-Yates">Ricardo Baeza-Yates</a> </span><em>Proc. DEXA-2001, </em><em> 2113, </em><em>page 102-111. </em><em>Springer-Verlag, </em>(<em>2001</em>)Fri Dec 14 02:44:08 CET 2007Proc. DEXA-20012113102-111Lecture Notes in Computer ScienceFlexible Comparison of Conceptual Graphs2001inf_retrieval semantic_closeness Conceptual graphs allow for powerful and computationally affordable representation of the semantic contents of natural language texts. We propose a method of comparison (approximate matching) of conceptual graphs. The method takes into account synonymy and subtype/supertype relationships between the concepts and relations used in the conceptual graphs, thus allowing for greater flexibility of approximate matching. The method also allows the user to choose the desirable aspect of similarity in the cases when the two graphs can be generalized in different ways. The algorithm and examples of its application are presented. The results are potentially useful in a range of tasks requiring approximate semantic or another structural matching ? among them, information retrieval and text mining.http://www.bibsonomy.org/bibtex/275561b4bb59407a24c6af5d62fb4d04c/diego_madiego_ma2007-12-14T02:44:07+01:00semantic_closeness <span class="authorEditorList"><a href="/author/y Gómez">Manuel Montes y Gómez</a>, <a href="/author/Gelbukh">Alexander Gelbukh</a>, and <a href="/author/López-López">Aurelio López-López</a> </span><em>Proc. DEXA-2002, </em><em> 2393, </em><em>page 122-136. </em><em>Springer-Verlag, </em>(<em>2002</em>)Fri Dec 14 02:44:07 CET 2007Proc. DEXA-20022393122-136Lecture Notes in Artificial IntelligenceText Mining at Detail Level Using Conceptual Graphs2002semantic_closeness Text mining is defined as knowledge discovery in large text collections. It detects interesting patterns such as clusters, associations, deviations, similarities, and differences in sets of texts. Current text mining methods use simplistic representations of text contents, such as keyword vectors, which imply serious limitations on the kind and meaningfulness of possible discoveries. We show how to do some typical mining tasks using conceptual graphs as formal but meaningful representation of texts. Our methods involve qualitative and quantitative comparison of conceptual graphs, conceptual clustering, building a conceptual hierarchy, and application of data mining techniques to this hierarchy in order to detect interesting associations and deviations. Our experiments show that, despite widespread misbelief, detailed meaningful mining with conceptual graphs is computationally affordable.http://www.bibsonomy.org/bibtex/25fdb9c18941f7a0880b94054e029f0e3/diego_madiego_ma2007-12-14T02:42:38+01:00lexical_resources semantic_closeness <span class="authorEditorList"><a href="/author/Litkowski">Kenneth C. Litkowski</a> </span><em>Proc. ACL-SIGLEX99, </em>(<em>1999</em>)Fri Dec 14 02:42:38 CET 2007Proc. ACL-SIGLEX99Towards a Meaning-Full Comparison of Lexical Resources1999lexical_resources semantic_closeness