BibSonomy publications for /author/CilibrasiMon Dec 05 00:00:00 CET 2011CoRRClustering by compressioncs.CV/03120442003dblp Mon Dec 05 00:00:00 CET 2011CoRRAbout the Lifespan of Peer to Peer Networksabs/cs/06120432006dblp Mon Dec 05 00:00:00 CET 2011CoRRThe Google Similarity Distanceabs/cs/04120982004dblp Mon Dec 05 00:00:00 CET 2011CoRRAlgorithmic Clustering of Musiccs.SD/03030252003dblp Mon Dec 05 00:00:00 CET 2011CoRRSimilarity of Objects and the Meaning of Wordsabs/cs/06020652006dblp Mon Dec 05 00:00:00 CET 2011CoRRNormalized Web Distance and Word Similarityabs/0905.40392009dblp Mon Dec 05 00:00:00 CET 2011CoRRA New Quartet Tree Heuristic for Hierarchical Clusteringabs/cs/06060482006dblp Mon Dec 05 00:00:00 CET 2011CoRRLimits of Rush Hour Logic Complexityabs/cs/05020682005dblp Mon Dec 05 00:00:00 CET 2011CoRRNormalized Information Distanceabs/0809.25532008dblp Wed Nov 23 16:24:13 CET 201115 Marchv2cs.CL/0412098370-383Automatic Meaning Discovery Using Google2004distance-measure google machine-learning We have found a method to automatically extract the meaning of words and phrases from the world-wide-web using Google page counts. The approach is novel in its unrestricted problem domain, simplicity of implementation, and manifestly ontological underpinnings. The world-wide-web is the largest database on earth, and the latent semantic context information entered by millions of independent users averages out to provide automatic meaning of useful quality. We demonstrate positive correlations, evidencing an underlying semantic structure, in both numerical symbol notations and number-name words in a variety of natural languages and contexts. Next, we demonstrate the ability to distinguish between colours and numbers, and to distinguish between 17th century Dutch painters; the ability to understand electrical terms, religious terms, and emergency incidents; we conduct a massive experiment in understanding WordNet categories; and finally we demonstrate the ability to do a simple automatic English-Spanish translation.Thu Apr 14 07:06:28 CEST 2011 Seattle, USA Proceedings of the IEEE International Symposium on Information Theory Automatic Extraction of Meaning from the Web 2006imported Thu Apr 14 07:06:28 CEST 2011 IEEE Transactions on Knowledge and Data Engineering 3 370-383 The Google Similarity Distance 19 2007imported Thu Apr 14 07:06:28 CEST 2011 Information Theory and Statistical Learning Normalized Information Distance 2009imported Tue Mar 08 08:43:28 CET 2011The Google Similarity Distance2007google similarity Fri Jan 28 11:34:03 CET 2011IEEE Transactions on Knowledge and Data Engineering370The Google Similarity Distance192007google relatedness_measures web_based imported 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.Fri Jan 28 11:32:21 CET 2011IEEE Transactions on Information TheoryApril4 1523-1545Clustering by compression512005toread compression clustering We present a new method for clustering based on compression. The method does not use subject-specific features or background knowledge, and works as follows: First, we determine a parameter-free, universal, similarity distance, the normalized compression distance or NCD, computed from the lengths of compressed data files (singly and in pairwise concatenation). Second, we apply a hierarchical clustering method. The NCD is not restricted to a specific application area, and works across application area boundaries. A theoretical precursor, the normalized information distance, co-developed by one of the authors, is provably optimal. However, the optimality comes at the price of using the noncomputable notion of Kolmogorov complexity. We propose axioms to capture the real-world setting, and show that the NCD approximates optimality. To extract a hierarchy of clusters from the distance matrix, we determine a dendrogram (ternary tree) by a new quartet method and a fast heuristic to implement it. The method is implemented and available as public software, and is robust under choice of different compressors. To substantiate our claims of universality and robustness, we report evidence of successful application in areas as diverse as genomics, virology, languages, literature, music, handwritten digits, astronomy, and combinations of objects from completely different domains, using statistical, dictionary, and block sorting compressors. In genomics, we presented new evidence for major questions in Mammalian evolution, based on whole-mitochondrial genomic analysis: the Eutherian orders and the Marsupionta hypothesis against the Theria hypothesis.Fri Dec 17 18:47:41 CET 2010May{The Google Similarity Distance}2007automatic-learning, google, linguistics, ontology, semantic 30{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.}Mon Dec 06 00:00:00 CET 2010Pattern Recognition3662-677A Fast Quartet tree heuristic for hierarchical clustering.442011dblp Fri Nov 26 09:32:26 CET 2010Information Theory and Statistical LearningNormalized Information Distance2009imported Fri Nov 26 09:32:26 CET 2010Seattle, USAProceedings of the IEEE International Symposium on Information TheoryAutomatic Extraction of Meaning from the Web2006imported