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	Latent semantic analysis - Wikipedia, the free encyclopedia
		Latent semantic analysis (LSA) is a technique in natural language processing, in particular in vectorial semantics, invented in 1990 [1] by Scott Deerwester, Susan Dumais, George Furnas, Thomas Landauer, and Richard Harshman.
		LSA uses a term-document matrix which describes the occurrences of terms in documents; it is a sparse matrix whose rows correspond to documents and whose columns correspond to terms, typically stemmed words that appear in the documents.
		A typical example of the weighting of the elements of the matrix is tf-idf: the element of the matrix proportional to the number of times the terms appear in each document, where rare terms are upweighted to reflect their relative importance.
	en.wikipedia.org /wiki/Latent_semantic_indexing (630 words)

	Latent semantic analysis -- Facts, Info, and Encyclopedia article (Site not responding. Last check: 2007-10-09)
		Latent semantic analysis (LSA) is a technique in (additional info and facts about information retrieval) information retrieval invented in 1990.
		LSA is a (additional info and facts about preprocessing) preprocessing step, used before the (The basic cognitive process of arranging into classes or categories) classification or (The activity of looking thoroughly in order to find something or someone) search of (Writing that provides information (especially information of an official nature)) documents.
		Thus, a newer alternative is probabilistic latent semantic analysis, based on a (A mathematical expression that is the sum of a number of terms) multinomial model, which is reported to give better results than standard LSA.
	www.absoluteastronomy.com /encyclopedia/l/la/latent_semantic_analysis.htm (480 words)

	Encyclopedia: Latent semantic analysis
		Scott Deerwester is one of the inventors of Latent semantic analysis.
		In the mathematical subfield of numerical analysis a sparse matrix is a matrix populated primarily with zeros.
		Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two{mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas.
	www.nationmaster.com /encyclopedia/Latent-semantic-analysis (1105 words)

	Meaning and context: the implications of LSA (latent semantic analysis) for semantics
		LSA has now corrected this situation by providing an objective, and in the case of language, a tested model of the relationship between meaning, usage constraints, and context.
		LSA’s success in accounting for this, and other diverse measured aspects of sentence and word comprehension, strongly suggests that the information contained in the higher-order associations is crucial to the human experience of what it is for some event, circumstance or thing to have meaning.
		LSA shows that when this is done the known words of a with sentences containing an unfamiliar word, this provides some rough information about the unknown word’s meaning.
	www.semioticon.com /frontline/meaningcontext.htm (6963 words)

	Elegant Report (Site not responding. Last check: 2007-10-09)
		Latent semantic analysis provided a solution to this problem by transforming the literal terms into a semantic space composed of the term document association.
		The two main goals of Latent Semantic Analysis (LSA) are to take the evidence from literal terms and project them into a semantic space whereby uncovering the underlying semantic/statistical meaning and to eliminate noise from the data by obtaining an approximation to the semantic space via dimensionality reduction [Deerwester 1990].
		Spelling correction using LSA can thus a competitive alternative to the Bayesian classifier and can be used to attack the problem of identifying contextual misuse of words, particularly when those words are the same part of speech.
	vims.cis.udel.edu /~mani/NLP/LSA.htm (1498 words)

	LSA: A Solution to Plato's Problem
		LSA uses no prior linguistic or perceptual similarity knowledge; it is based solely on a general mathematical learning method that achieves powerful inductive effects by extracting the right number of dimensions (e.g., 300) to represent objects and contexts.
		The first step of the LSA analysis is to transform each cell entry from the number of times that a word appeared in a particular context to the log of that frequency.
		LSA as currently developed, is, of course, mute on the temporal dynamics of comprehension, but it does provide an objective way to represent, simulate and assess the degree of semantic similarity between words and between words and longer passages.
	lsa.colorado.edu /papers/plato/plato.annote.html (19690 words)

	InfoVis CyberInfrastructure- Latent Semantic Analysis
		Latent Semantic Analysis (LSA) can be applied to induce and represent aspects of the meaning of words (Berry et al., 1995; Deerwester et al., 1990; Landauer and Dumais, 1997; Landauer et al.,1998).
		LSA is a variant of the vector space model that converts a representative sample of documents to a term-by-document matrix in which each cell indicates the frequency with which each term (rows) occurs in each document (columns).
		LSA extends the vector space model by modeling term-document relationships using a reduced approximation for the column and row space computed by the singular value decomposition of the term by document matrix.
	iv.slis.indiana.edu /sw/lsa.html (1985 words)

	ipedia.com: Latent semantic analysis Article (Site not responding. Last check: 2007-10-09)
		Latent semantic analysis is a technique in information retrieval invented in 1990 [1].
		Latent semantic analysis (LSA) is a technique in information retrieval invented in 1990 [1].
		LSA is a pre-processing step, used before the classification or search of documents.
	www.ipedia.com /latent_semantic_analysis.html (495 words)

	Due Diligence
		Latent semantic analysis (or indexing) is an application of what's called principal components analysis (PCA), or factors analysis, to the domain of information organization.
		Latent Semantics was invented at Bellcore in late 1980s, by an All-Star information science team including Tom Landauer, George Furnas, and Sue Dumais.
		The simplest approach is to use a related factor analysis technique to find the best fit to predicting spam/not-spam in a training sample; it's not a full PCA but I suppose you could call it latent semantics.
	www.pacificavc.com /blog/2003/02/10.html (1443 words)

	IngentaConnect Use of latent semantic analysis for predicting psychological phen... (Site not responding. Last check: 2007-10-09)
		Latent semantic analysis (LSA) is a computational model of human knowledge representation that approximates semantic relatedness judgments.
		LSA indices of similarity should then be derived from this theoretical understanding.
		Second, the knowledge base (semantic space) from which similarity indices are generated must contain "knowledge" that is appropriate to the task at hand.
	api.ingentaconnect.com /content/psocpubs/brm/2003/00000035/00000001/art00003 (236 words)

	Using LSA FAQ (Site not responding. Last check: 2007-10-09)
		Latent semantic analysis is a technique for comparing text similarity that was developed (and patented) by Bellcore.
		LSA doesn't directly tell you what the meaning of a certain text is, or its function in the dialogue or anything like that.
		LSA is the center of his technique, comparing new student texts to previously graded student texts.
	www.msci.memphis.edu /~wiemerhp/trg/lsa-followup.html (4238 words)

	LTIC Chapter 5
		They are pursuing investigations of Latent Semantic Analysis (LSA) that range from modeling second-language acquisition to the use of LSA as a way to generate summaries of text.
		Latent semantic analysis (LSA) is a statistical technique used to extract the deep meaning of patterns of words in specific contexts of use.
		LSA provides a high-dimensional (yet still reduced in dimensions as compared with "reality") representation of the associations between words and the documents containing those words.
	wildcat.iat.sfu.ca /ltic/chapter5.html (5315 words)

	@semantics weblog: Latent Semantic Analysis (LSA)
		A recent article on the Guardian goes a step further talking about Latent Semantic Indexing (LSI) which apply LSA to search engines - nice quick summary about LSA and how could be used for automatic metadata extraction and stuff like that.
		LSA extracts the semantic similarity between words as well as between documents based on contextual usage of words in documents as represented by a word-by-document matrix whose entries are frequency counts to begin with.
		Here it is used to capture the semantic fabric of spoken document and thus reduce the word error rate.
	blog.asemantics.com /archives/000012.html (348 words)

	An Introduction to Latent Semantic Analysis by Patrick Kellogg
		LSA uses a matrix decomposition algorithm to reduce the dimensionality.
		LSA is an automatic mathematical algorithm for extracting relationships in word usage in passages.
		LSA is intuitively sensible, with a three-fourths gain in total comprhension vocabulary inferred from knowledge about words not in the passage or paragraph.
	www.patrickkellogg.com /school/papers/LSA.htm (698 words)

	DOCUMENT SPACE MODELS USING LATENT SEMANTIC ANALYSIS (Site not responding. Last check: 2007-10-09)
		To this end, a technique known as latent semantic analysis (LSA) is used.
		The principal contribution of this work is to characterize the document space resulting from the LSA modeling and to demonstrate the approach for mixture LM application.
		It is shown that, using semantic information, mixture LMs performs better than a conventional single LM with slight increase of computational cost.
	www.dcs.shef.ac.uk /~sjr/pubs/1997/eurosp97-lsa.html (172 words)

	References to Papers on LSI
		Latent Semantic Indexing (LSI) is a novel information retrieval method developed at Telcordia that improves your ability to find relevant information.
		Landauer, T. and Littman, M. "Fully automatic cross-language document retrieval using latent semantic indexing." In Proceedings of the Sixth Annual Conference of the UW Centre for the New Oxford English Dictionary and Text Research, pp.
		Story, R. "An explanation of the effectiveness of latent semantic indexing by means of a Bayesian regression model".
	lsi.argreenhouse.com /lsi/LSIpapers.html (839 words)

	Landuar 1998: Learning and Representing verbal meaning: The latent semantic analysis theory (Site not responding. Last check: 2007-10-09)
		The LSA, based on mathematical and computational models, expresses ideas about word meaning based on the relations between word meanings in a semantic space.
		LSA creates a semantic space and establishes links between each word type in that space.
		Among other applications, LSA has been shown to represent passage meanings as well as word meanings as a single point in its semantic space.
	www.cc.gatech.edu /~jimmyd/summaries/landaur1998.html (416 words)

	Hybrid Pre-Query Term Expansion using Latent Semantic Analysis (Site not responding. Last check: 2007-10-09)
		Latent semantic retrieval methods (unlike vector space methods) take the document and query vectors and map them into a topic space to cluster related terms and documents.
		Since we have the latent semantic data in a mapping, we are able to store and retrieve vector information in the same fast manner that the vector space method offers.
		Multiple mappings are combined to produce hybrid latent semantic retrieval which provide precision results 5% greater than the vector space method and fast query times.
	csdl2.computer.org /persagen/DLAbsToc.jsp?resourcePath=/dl/proceedings/&toc=comp/proceedings/icdm/2004/2142/00/2142toc.xml&DOI=10.1109/ICDM.2004.10085 (242 words)

	Readings in Latent Semantic Analysis (Site not responding. Last check: 2007-10-09)
		Landauer, T.K. Applications of Latent Semantic Analysis, 24th Annual Meeting of the Cognitive Science Society, August 9th 2002.
		Shapiro, A.M., and McNamara, D.S. The use of latent semantic analysis as a tool for the quantitative assessment of understanding and knowledge.
		Latent semantic analysis and the construction of coherent extracts.
	www-leibniz.imag.fr /perso/s1/blemaire/public_html/lsa.html (2213 words)

	Using Latent Semantic Analysis to assess knowledge: Some technical considerations - Rehder, Schreiner, Wolfe, Laham, ... (Site not responding. Last check: 2007-10-09)
		We did this by comparing an essay written by a student with one or more target instructional texts in terms of the cosine between the vector representation of the student's essay and the instructional text in...
		Using Latent Semantic Analysis To Aid Speech Recognition And..
		The Measurement of Textual Coherence with Latent Semantic..
	citeseer.lcs.mit.edu /rehder98using.html (408 words)

	Structured Latent Semantic Analysis (Site not responding. Last check: 2007-10-09)
		Structured Latent Semantic Analysis (SLSA) is a response to the failures of traditional syntactic natural language understanding (NLU) and of statistical, corpus-based semantic NLU.
		The semantics are taken from LSA which uses word occurrence information in a large corpus to create vectors in a high dimensional space for each word and each document in the corpus.
		Previous uses of LSA have shown that it works well with single words, and longer texts, but not with single sentences.
	www.cogsci.ed.ac.uk /~peterwh/slsa (228 words)

	Using Latent Semantic Analysis on Semantic Attributes.
		Each dimension in the reduced space is a latent variable (or factor) representing groups of highly correlated index terms.
		Furthermore, the generated latent variables represent groups of highly correlated attributes in the original data, thus potentially reducing the amount of noise associated with the semantic information.
		As we will illustrate in the next section, performing latent semantic analysis on the semantic space, generally leads to substantial gains in prediction accuracy based on the semantic attributes.
	maya.cs.depaul.edu /~mobasher/papers/ewmf04-web/node8.html (277 words)

	Latent Semantic Indexing
		Latent semantic indexing adds an important step to the document indexing process.
		LSI considers documents that have many words in common to be semantically close, and ones with few words in common to be semantically distant.
		The theoretical is one thing, and the theoretical, academic and scientific side is another; the practical application on the level of implemtation of principles from a primarily commercial standpoint, however, is quite another matter, and as you undoubtedly are aware of, is worthy of serious consideration.
	forums.searchenginewatch.com /showthread.php?p=33055 (3782 words)

	Latent Semantic Analysis for User Modeling (ResearchIndex)
		Abstract: Latent Semantic Analysis (LSA) is a tool for extracting semantic information from texts as well as a model of language learning based on the exposure to texts.
		Domain examples and student productions are represented in a high-dimensional semantic space, automatically built from a statistical analysis of the co-occurrences of their lexemes.
		12 The Measurement of Textual Coherence with Latent Semantic An..
	citeseer.ist.psu.edu /678571.html (449 words)

	Latent Semantic Analysis (LSA) - Crawl into the Google Algorithm?
		For example, using LSA, a search engine would recognize that trips to the zoo often include viewing wildlife and animals, possibly as part of a tour.
		They cannot, for example, learn through their index that Zebras and Tigers are both examples of striped animals, although they may realize that stripes and zebra are more semanticly connected then ducks and stripes.
		Which I took as a kind of "Is this statement true or false?" type of deal and answered in terms of the simplest forms of semantic analysis.
	www.seroundtable.com /archives/001478.html (690 words)

	Latent Semantic Analysis (LSA)
		The latest newsletter from Axandra has everything you need to know about LSI (latent semantic indexing) and the impact it can have, if in fact, Google is using it in its algorithm.
		Latent Semantic Analysis (LSA) is a theory and method for extracting and...
		Latent semantic analysis (LSA) is a technique in natural language processing, in particular in vectorial semantics, invented in 1990 [1] by Scott Deerwester...
	www.webrankinfo.com /english/seo-news/topic-383.htm (319 words)

	Event \| Latent Semantic Analysis (LSA) based Language Models for Meetings (Site not responding. Last check: 2007-10-09)
		LSA models have been successfully applied for different tasks.
		LSA models long distance semantic dependencies of words in a constructed semantic space via Singular Value Decomposition (SVD).
		As meetings have specific topics, LSA models could be useful for dynamic topic adaptation.
	www.icsi.berkeley.edu /cgi-bin/events/event.pl?ID=000289 (88 words)

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