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Topic: Entropy (disambiguation)

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Thermodynamic entropy

Thermodynamics

William Thomson, 1st Baron Kelvin

Heat

Entropy

	Entropy (disambiguation) - Wikipedia, the free encyclopedia
		Entropy is a measure of biodiversity in the study of biological ecology.
		Entropy is the name of the split EP released by the two bands, Anathallo and Javelins, by Potential Getaway Driver in December of 2005.
		Organisational entropy has been introduced by Martin J. Klein (1953) and is used in the context of organisation theory, organisational studies, and information theory, as a measure of the disorder in the context of operational processes that are conducted in these organisations or in any of their producing entities.
	en.wikipedia.org /wiki/Entropy_(disambiguation) (673 words)

	Entropy - Wikipedia, the free encyclopedia
		In statistical thermodynamics, entropy is envisioned as a measure of the statistical "mixedupness" or "disorder" of the thermodynamic system, the amount of uncertainty that would remain about the exact microscopic state of the system, given a description of its macroscopic properties.
		The entropy depends only on the current state of the system, not its detailed previous history, and so it is a state function of the parameters like pressure, temperature, etc., which describe the observable macroscopic properties of the system.
		In the 1877, thermodynamicist Ludwig Boltzmann visualized a probabilistic way to measure the entropy of the ensemble of ideal gas particles, in which he defined entropy to be proportional to the logarithm of the number of microstates such a gas could occupy.
	en.wikipedia.org /wiki/Thermodynamic_entropy (2013 words)

	Entropy: Encyclopedia topic (Site not responding. Last check: 2007-10-14)
		This is due to the fact that a system at zero temperature exists in its ground state, so that its entropy is determined by the degeneracy (degeneracy: The state of being degenerate in mental or moral qualities) of the ground state.
		The entropy in such a state would be that of a classical ideal gas plus contributions from molecular rotations and vibrations, which may be determined spectroscopically (spectroscopically: spectroscopy is the study of spectra, ie....
		For instance, a calculation of the entropy of ice (ice: Water frozen in the solid state) by the latter method, assuming no entropy at zero temperature, falls short of the value obtained with a high-temperature reference state by 3.41 J/(mol·K).
	www.absoluteastronomy.com /reference/entropy (2980 words)

	Black hole - Biocrawler (Site not responding. Last check: 2007-10-14)
		Therefore, Jacob Bekenstein proposed that a fl hole should have an entropy and that it should be proportional to its horizon area.
		Using the first law of fl hole mechanics, it follows that the entropy of a fl hole is one quarter of the area of the horizon.
		It was later suggested that fl holes are maximum-entropy objects, meaning that the maximum entropy of a region of space is the entropy of the largest fl hole that can fit into it.
	www.biocrawler.com /encyclopedia/Black_hole (4386 words)

	Andrei Mikheev Papers (Site not responding. Last check: 2007-10-14)
		In this paper we present an approach to tackle three important problems of text normalization: sentence boundary disambiguation, disambiguation of capitalized words when they are used in positions where capitalization is expected, and identification of abbreviations.
		In this paper we present an approach to the disambiguation of capitalized words when they are used in the positions where capitalization is expected, such as the first word in a sentence or after a period, quotes, etc..
		Maximum entropy framework proved to be expressive and powerful for the statistical language modelling, but it suffers from the computational expensiveness of the model building.
	www.ltg.ed.ac.uk /~mikheev/papers.html (3313 words)

	Projects (Site not responding. Last check: 2007-10-14)
		Highly precise disambiguation of analyses is a key problem and prerequiste for real-world applications for broad-coverage parsing.
		The goal of the COMET project is to apply statistical machine learning techniques to induce disambiguation routines for broad-coverage constraint-based parsers automatically from data.
		Depending on the availability of data (fully labeled, partially labeled, unlabeled) and the complexity of the ambiguity space of the grammars different estimators have been invented, implemented, and evaluated.
	www2.parc.com /istl/groups/nltt/comet/comet.html (532 words)

	CPS 370 - FALL 1997
		Unsupervised learning of disambiguation rules for part of speech tagging.
		Disambiguating noun groupings with respect to WordNet senses.
		Word sense disambiguation using a second language monolingual corpus.
	www.cs.duke.edu /~mlittman/courses/cps370-97 (1176 words)

	Minkowski-Bouligand dimension: Encyclopedia topic (Site not responding. Last check: 2007-10-14)
		The upper box dimension is sometimes called the entropy dimension, Kolmogorov dimension, or upper Minkowski dimension, while the lower box dimension is also called the lower Minkowski dimension.
		The logarithm (logarithm: The exponent required to produce a given number) of the packing and covering numbers are sometimes referred to as entropy numbers, and are somewhat analogous (though not identical) to the concepts of thermodynamic entropy (thermodynamic entropy: :for other uses of the term entropy, see entropy (disambiguation)...
		[follow hyperlink for more...]) and information-theoretic entropy (information-theoretic entropy: entropy is a concept in thermodynamics (see thermodynamic entropy), statistical...
	www.absoluteastronomy.com /reference/minkowski-bouligand_dimension (1323 words)

	Combining Knowledge- and Corpus-based Word-Sense-Disambiguation Methods (Site not responding. Last check: 2007-10-14)
		In this paper we concentrate on the resolution of the lexical ambiguity that arises when a given word has several different meanings.
		The task of WSD consists of assigning the correct sense to words using an electronic dictionary as the source of word definitions.
		Our hypothesis is that word-sense disambiguation requires several knowledge sources in order to solve the semantic ambiguity of the words.
	www.cs.cmu.edu /afs/cs/project/jair/pub/volume23/montoyo05a-html/Montoyo05a.html (197 words)

	MxTerminator
		The next step in the pipeline is to perform complete sentence boundary disambiguation.
		The sentence boundary disambiguation module inserts a tag pair around each sentence it encounters.
		MxTerminator is different from many sentence boundary disambiguation tools in that instead of a set of regular expressions or other rules it uses a statistical approach based on maximum entropy.
	www.cs.umd.edu /Honors/reports/Elkis-honorspaper/node6.html (276 words)

	(NP (JJ Natural) (NN Language) (NN Processing) (JJ Final) (NN Project)) (Site not responding. Last check: 2007-10-14)
		Word Sense Disambiguation (WSD) is an important problem in Natural Language Processing which has relevance to many other problems in the field.
		After this, the constituents corresponding to the unambiguous word or phrase in the parse tree are replaced with the constituent for the ambiguous word, and the resulting parse is compared to a gold standard for performance evaluation.
		The disambiguation of the words was done by hand with help from the Princeton WordNet and Dictionary.com, and the Penn Treebank Wall Street Journal corpus was used a source of data.
	www-personal.umich.edu /~atury/nlp (336 words)

	Untitled Document (Site not responding. Last check: 2007-10-14)
		entropy as average (positive) log probability; cross-entropy; K-L divergence as a reasonable measure of the relationship between two distributions.
		Yarowsky, D. ``Word Sense Disambiguation.'' In R. Dale, H. Moisl and H. Somers (eds.) The Handbook of Natural Language Processing.
		Yarowsky, D. Unsupervised Word Sense Disambiguation Rivaling Supervised Methods.'' In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics.
	humanities.uchicago.edu /faculty/goldsmith/CompLingCourse (2590 words)

	A Probabilistic Approach to Lexical Semantic Knowledge Acquisition and Structural Disambiguation (ResearchIndex) (Site not responding. Last check: 2007-10-14)
		Abstract: Structural disambiguation in sentence analysis is still a central problem in natural language processing.
		147 Word-sense disambiguation using statistical models of Roget'..
		70 A maximum entropy approach to adaptive statistical language..
	citeseer.ist.psu.edu /li98probabilistic.html (2255 words)

	[No title] (Site not responding. Last check: 2007-10-14)
		This principle states that speakers produce text such that the entropy rate remains constant from sentence to sentence.
		This makes a number of predictions: (a) entropy rate and processing effort are correlated, (b) in connected text, processing effort is independent of sentence position, and (c) for isolated sentences, entropy rate (and hence processing effort) increases with sentence position.
		Using a corpus of eye-tracking data, we show that predictions (a) and (b) are borne out.
	www.ipam.ucla.edu /abstract.aspx?tid=5354 (316 words)

	Publications of Jun Wu
		Sanjeev Khudanpur and Jun Wu, A Maximum Entropy Language Model to Integrate N-Grams and Topic Dependencies for Conversational Speech Recognition.
		Jun Wu and Zuoying Wang, Entropy of Chinese and the Perplexity of the Language Models, ACTA Electronica Sinica, v24 n10 Oct 1996, p69-71.(EI)
		Jun Wu, A Maximum Entropy Language Model with Topic Sensitive Features, Qualification Project Report of the Johns Hopkins University.
	www.cs.jhu.edu /~junwu/publications.html (628 words)

	SDSUniverse \| Stochastic Unification-Based Grammars
		Since maximum entropy models impose no unwarranted independence assumptions,they are well suited for capturing the kinds of interactions found in unification-based grammars.
		A further benefit of maximum entropy models is that they allow stochastic rule systems to be augmented with
		However, the richness of the representations is not without cost: even modest maximum entropy models can require considerable computational resources and very large quantities of annotated training data in order to accurately estimate the model's parameters.
	www.sdsuniverse.info /info_content_event.asp?id=1957 (170 words)

	[No title] (Site not responding. Last check: 2007-10-14)
		Techniques working well in the area of POS tagging are also likely to work well in a large range of other NLP problems such as word sense disambiguation and discourse segmentation when reliable annotated corpora for these problems become available.
		In this paper we continue the systematic comparison of the best two systems, and investigate whether the difference is due to the algorithms or to the information sources used by the systems.
		Ratnaparkhi, A. `A maximum entropy part of speech tagger.' Conference on empirical methods in natural language processing, University of Pennsylvania, 1996.
	www.ccl.kuleuven.ac.be /clin98/abstr/pauw.html (372 words)

	maxent (Site not responding. Last check: 2007-10-14)
		In Section 2 we give an overview of the maximum entropy philosophy and work through a motivating example.
		In Section 3 we describe the mathematical structure of maximum entropy models and give an efficient algorithm for estimating the parameters of such models.
		Finally, in Section 5 we describe the application of maximum entropy ideas to several tasks in stochastic language processing: bilingual sense disambiguation, word reordering, and sentence segmentation.
	linguistics.byu.edu /classes/ling480dl/maxent.htm (483 words)

	Anoop Sarkar: CMPT 882-3 - Statistical Learning of Natural Language (Site not responding. Last check: 2007-10-14)
		A simple introduction to maximum entropy models for natural language processing (1997).
		A Gaussian prior for smoothing maximum entropy models (1999).
		A rule based approach to prepositional phrase attachment disambiguation (1994).
	www.cs.sfu.ca /~anoop/courses/CMPT-882-Fall-2002 (1559 words)

	Brown CS - CS241 (Site not responding. Last check: 2007-10-14)
		This course covers statistical methods for learning a natural language and applying the knowledge to specific tasks.
		Topics include: entropy and cross entropy of a language, hidden Markov models, Viterbi algorithm, forward-backward algorithm, trigram models, part-of-speech tagging, probabilistic context-free parsing, inside-outside algorithm, learning probabilistic context-free grammars, statistical models of syntactic disambiguation, statistical anaphora resolution, deriving semantic word classes from statistical properties, and word-sense disambiguation.
		Grading is based primarily on the project, and secondarily on the two in-class, 40 minute, exams.
	www.cs.brown.edu /courses/cs241 (137 words)

	Final Revised ACL/EACL97 Registration Brochure (Site not responding. Last check: 2007-10-14)
		MAXIMUM ENTROPY MODELING FOR NATURAL LANGUAGE, 3:00PM--6:30PM Eric Sven Ristad, Princeton University The maximum entropy framework is a powerful method for building statistical models of natural language.
		We review the maximum entropy framework, explore the art of effective feature design, and show how to implement models using the instructor's publicly available Maximum Entropy Modeling Toolkit.
		Logical Approaches to Syntactic Theories, 3:00PM--6:30PM James Rogers, University of Central Florida Thomas Cornell, SFB 340, University of Tuebingen The trend, over the last ten or fifteen years, has been towards specifying syntactic structures in terms of constraints on their form rather than via mechanisms for generating them.
	www.elsnet.org /list/may97/5.20May97.html (4442 words)

	Untitled Document (Site not responding. Last check: 2007-10-14)
		For additional readings and resources, see the syllabus for our seminar on machine translation (Spring 2005).
		This is not an assignment as such: Find and download files at least 25K in length in at least 3 languages using an alphabetic writing system.
		Using a spreadsheet if you wish, or a program that you write, calculate the frequencies of each of the letters, the log frequencies, and the entropy of each text.
	hum.uchicago.edu /~jagoldsm/CompLing (3594 words)

	M G Dyer - Selected Publications
		Chao, G. and Dyer, M. Maximum Entropy Models for Word Sense Disambiguation.
		Chao, G. and Dyer, M. Word Sense Disambiguation of Adjectives Using Probabilistic Networks.
		Disambiguation and Language Acquisition through the Phrasal Lexicon.
	www.cs.ucla.edu /~dyer/Publications.html (2221 words)

	[No title] (Site not responding. Last check: 2007-10-14)
		Classes 6-8 Information theory Entropy, joint entropy, conditional entropy.
		Classes 9-10 Data compression and coding Entropy rate.
		Classes 19-20 Word sense disambiguation and lexical acquisition Supervised disambiguation.
	tangra.si.umich.edu /~radev/760f00/syllabus.txt (327 words)

	Toutanova, Kristina; Manning, Christopher: Enriching the Knowledge Sources used in a Maximum Entropy Part-of-Speech ... (Site not responding. Last check: 2007-10-14)
		This paper presents results for a maximum-entropy-based part of speech tagger, which achieves superior performance principally by enriching the information sources used for tagging.
		In particular, we get improved results by incorporating these features: (i) more extensive treatment of capitalization for unknown words; (ii) features for the disambiguation of the tense forms of verbs; (iii) features for disambiguating particles from prepositions and adverbs.
		Enriching the Knowledge Sources used in a Maximum Entropy Part-of-Speech tagger, Proceedings of the 2000 Joint SIGDAT Conference EMNLP/VLC, 63-71, 2000
	dbpubs.stanford.edu:8090 /pub/2000-39 (234 words)

	GraphLing: Description of Proposed Work (Site not responding. Last check: 2007-10-14)
		A Method for Disambiguating Word Senses in a Large Corpus.
		[36] Hirschberg, J. and Litman, D. Empirical Studies on the Disambiguation of Cue Phrases.
		Adaptive Language Modeling Using the Maximum Entropy Principle.
	crl.nmsu.edu /Research/Projects/graphling/Proposal/node8.html (1221 words)

	The Role of Algorithm Bias vs Information Source in Learning Algorithms for Morphosyntactic Disambiguation (Site not responding. Last check: 2007-10-14)
		Morphosyntactic Disambiguation (Part of Speech tagging) is a useful benchmark problem for system comparison because it is typical for a large class of Natural Language Processing (NLP) problems that can be defined as {\em disambiguation in local context}.
		This paper adds to the literature on the systematic and objective evaluation of different methods to automatically learn this type of disambiguation problem.
		Results indicate that earlier observed differences in accuracy can be attributed largely to differences in information sources used, rather than to algorithm bias.
	www.cnts.ua.ac.be /conll2000/abstracts/01924dep.html (175 words)

	Statistical Natural Language Processing: Models and Methods (CS775)
		Hiding a semantic hierarchy in a Markov model.
		- definitions and axioms for entropy - cross entropy when an unknown source is involved - relative entropy/Kullback Leibler divergence to measure distributional similarity - mutual information as a special case - can the mutual information be negative?
		Republished as "The mathematical theory of communication" in Warren Weaver and Claude E. Shannon, eds., The Mathematical Theory of Communication, U. Illinois Press, 1949.
	www.cs.cornell.edu /courses/cs775/2001sp (1119 words)

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