What is SEMANTIC FEATURE? What does SEMANTIC FEATURE mean? SEMANTIC FEATURE meaning - SEMANTIC FEATURE definition - SEMANTIC FEATURE explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Semantic features represent the basic conceptual components of meaning for any lexical item. An individual semantic feature constitutes one component of a word's intension, which is the inherent sense or concept evoked. Linguistic meaning of a word is proposed to arise from contrasts and significant differences with other words. Semantic features enable linguistics to explain how words that share certain features may be members of the same semantic domain. Correspondingly, the contrast in meanings of words is explained by diverging semantic features. For example, father and son share the common components of 'human', 'kinship', 'male' and are thus part of a semantic domain of male family relations. They differ in terms of 'generation' and 'adulthood', which is what gives each its individual meaning. The analysis of semantic features is utilized in the field of linguistic semantics, more specifically the subfields of lexical semantics, and lexicology. One aim of these subfields is to explain the meaning of a word in terms of their relationships with other words. In order to accomplish this aim, one approach is to analyze the internal semantic structure of a word as composed of a number of distinct and minimal components of meaning. This approach is called componential analysis, also known as semantic decomposition. Semantic decomposition allows any given lexical item to be defined based on minimal elements of meaning, which are called semantic features. The term semantic feature is usually used interchangeably with the term semantic component. Additionally, semantic features/semantic components are also often referred to as semantic properties. The theory of componential analysis and semantic features is not the only approach to analyzing the semantic structure of words. An alternative direction of research that contrasts with componential analysis is prototype semantics.
Views: 2990 The Audiopedia
An explication of the difference between syntax and semantics in philosophy of language, linguistics, and computer science. Information for this video gathered from The Stanford Encyclopedia of Philosophy, The Internet Encyclopedia of Philosophy, The Cambridge Dictionary of Philosophy, The Oxford Dictionary of Philosophy and more! Information for this video gathered from The Stanford Encyclopedia of Philosophy, The Internet Encyclopedia of Philosophy, The Cambridge Dictionary of Philosophy, The Oxford Dictionary of Philosophy and more! (#Syntax #Semantics)
Views: 59049 Carneades.org
What is LEXICAL SEMANTICS? What does LEXICAL SEMANTICS mean? LEXICAL SEMANTICS meaning - LEXICAL SEMANTICS definition - LEXICAL SEMANTICS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Lexical semantics (also known as lexicosemantics), is a subfield of linguistic semantics. The units of analysis in lexical semantics are lexical units which include not only words but also sub-words or sub-units such as affixes and even compound words and phrases. Lexical units make up the catalogue of words in a language, the lexicon. Lexical semantics looks at how the meaning of the lexical units correlates with the structure of the language or syntax. This is referred to as syntax-semantic interface. The study of lexical semantics looks at: - the classification and decomposition of lexical items, - the differences and similarities in lexical semantic structure cross-linguistically, - the relationship of lexical meaning to sentence meaning and syntax. Lexical units, also referred to as syntactic atoms, can stand alone such as in the case of root words or parts of compound words or they necessarily attach to other units such as prefixes and suffixes do. The former are called free morphemes and the latter bound morphemes. They fall into a narrow range of meanings (semantic fields) and can combine with each other to generate new meanings. Lexical items contain information about category (lexical and syntactic), form and meaning. The semantics related to these categories then relate to each lexical item in the lexicon. Lexical items can also be semantically classified based on whether their meanings are derived from single lexical units or from their surrounding environment. Lexical items participate in regular patterns of association with each other. Some relations between lexical items include hyponymy, hypernymy, synonymy and antonymy, as well as homonymy.
Views: 7949 The Audiopedia
Views: 563883 Gate Lectures by Ravindrababu Ravula
In linguistics, semantics is the study of the interpretation of signs or symbols used in agents or communities within particular circumstances and contexts. Within this view, sounds, facial expressions, body language, and proxemics have semantic (meaningful) content, and each comprises several branches of study. In written language, things like paragraph structure and punctuation bear semantic content; other forms of language bear other semantic content. The formal study of semantics intersects with many other fields of inquiry, including lexicology, syntax, pragmatics, etymology and others. Independently, semantics is also a well-defined field in its own right, often with synthetic properties. In the philosophy of language, semantics and reference are closely connected. Further related fields include philology, communication, and semiotics. The formal study of semantics can therefore be manifold and complex. Semantics contrasts with syntax, the study of the combinatorics of units of a language (without reference to their meaning), and pragmatics, the study of the relationships between the symbols of a language, their meaning, and the users of the language. Semantics as a field of study also has significant ties to various representational theories of meaning including truth theories of meaning, coherence theories of meaning, and correspondence theories of meaning. Each of these is related to the general philosophical study of reality and the representation of meaning. Thanks for watching. Please subscribe to my channel.
Views: 30420 English Literature Hub
The paper "The Semantic Structure of the Slovak Physics Textbook" has been presented in the framework of the Seventh edition of the International Conference "New Perspectives in Science Education" held in Florence on 22 - 23 March 2018
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What is STRUCTURAL SEMANTICS? What does STRUCTURAL SEMANTICS mean? STRUCTURAL SEMANTICS meaning - STRUCTURAL SEMANTICS definition - STRUCTURAL SEMANTICS explanation. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Logical positivism asserts that structural semantics is the study of relationships between the meanings of terms within a sentence, and how meaning can be composed from smaller elements. However, some critical theorists suggest that meaning is only divided into smaller structural units via its regulation in concrete social interactions; outside of these interactions, language may become meaningless. Structural semantics is that branch that marked the modern linguistics movement started by Ferdinand de Saussure at the break of the 20th century in his posthumous discourse titled "Cours De Linguistique Generale" (A Course in General Linguistics). He posits that language is a system of inter-related units and structures and that every unit of language is related to the others within the same system. His position later became the bedding ground for other theories such as componential analysis and relational predicates. Structuralism is a very efficient aspect of Semantics, as it explains the concordance in the meaning of certain words and utterances. The concept of sense relations as a means of semantic interpretation is an offshoot of this theory as well. Structuralism has revolutionized semantics to its present state, and it also aids to the correct understanding of other aspects of linguistics. The consequential fields of structuralism in linguistics are sense relations (both lexical and sentential) among others.
Views: 296 The Audiopedia
by Professor Naftali tishby
Views: 343 ELSC Video
How are lexemes and objects related? How can we define the relationships between the lexemes of a language? These questions are central to word semantics and defineits main branches reference and sense. This E-Lecture provides an overview of these main areas of word semantics.
Views: 53990 The Virtual Linguistics Campus
Sometimes a single sentence has more than one meaning. A group of linguists explore prepositional phrase attachment ambiguity. Twitter @lingvids LingVids is created by Caroline Andrews, Leland Paul Kusmer, Gretchen McCulloch, and Joshua Levy. For a more detailed introduction to syntax, see the How to Draw Syntax Trees series starting at: http://allthingslinguistic.com/post/100357884082/how-to-draw-syntax-trees-part-1-so-you-asked Music is composed by Kevin MacLeod and used under a Creative Commons License. The track can be found here: https://www.youtube.com/watch?v=V8CAH0vsoPM LingVids is no longer being updated, but one of its creators is now cohosting a linguistics podcast called Lingthusiasm. You can listen to it on youtube, iTunes, soundcloud, or wherever else you get your podcasts.
Views: 32935 Ling Vids
Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics. Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/ Twitter: https://twitter.com/deeplearningtv Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency. Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words. One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word. The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector. Two popular tools: Word2Vec: https://code.google.com/archive/p/word2vec/ Glove: http://nlp.stanford.edu/projects/glove/ Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse. Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language. Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis: “He turned around a team otherwise known for overall bad temperament” In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive. Credits Nickey Pickorita (YouTube art) - https://www.upwork.com/freelancers/~0147b8991909b20fca Isabel Descutner (Voice) - https://www.youtube.com/user/IsabelDescutner Dan Partynski (Copy Editing) - https://www.linkedin.com/in/danielpartynski Marek Scibior (Prezi creator, Illustrator) - http://brawuroweprezentacje.pl/ Jagannath Rajagopal (Creator, Producer and Director) - https://ca.linkedin.com/in/jagannathrajagopal
Views: 45650 DeepLearning.TV
Compiler Design lecture | Semantic Analysis | various Phases of compiler Lexical Analysis Syntax Analysis Semantic Analysis Intermediate Code Generation Code Optimization Target Machine Code Generation The semantic analyzer uses the syntax tree and the information in the symbol table to check the source program for semantic consistency with the language definition. It also gathers type information and saves it in either the syntax tree or the symbol table, for subsequent use during intermediate-code generation. An important part of semantic analysis is type checking, where the compiler checks that each operator has matching operands. For example, many program- ming language definitions require an array index to be an integer; the compiler must report an error if a floating-point number is used to index an array. The language specification may permit some type conversions called coer- cions. For example, a binary arithmetic operator may be applied to either a pair of integers or to a pair of floating-point numbers. If the operator is applied to a floating-point number and an integer, the compiler may convert or coerce the integer into a floating-point number.
Views: 22818 Gate Instructors
Xiao Yang; Ersin Yumer; Paul Asente; Mike Kraley; Daniel Kifer; C. Lee Giles We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images. We consider document semantic structure extraction as a pixel-wise segmentation task, and propose a unified model that classifies pixels based not only on their visual appearance, as in the traditional page segmentation task, but also on the content of underlying text. Moreover, we propose an efficient synthetic document generation process that we use to generate pretraining data for our network. Once the network is trained on a large set of synthetic documents, we fine-tune the network on unlabeled real documents using a semi-supervised approach. We systematically study the optimum network architecture and show that both our multimodal approach and the synthetic data pretraining significantly boost the performance.
Views: 710 ComputerVisionFoundation Videos
An overview of the various levels of linguistic analysis that discourse analysts use in their work. Includes discussion and examples of phonology, morphology, syntax, semantics, and pragmatics.
This introductory E-Lecture about sentence semantics introduces the main principles and the central mechanisms involved in propositional and predicate logic. Additionally, it shows how entailment relations can be defined and applied and how the principles of quantification can be combined with predicates.
Views: 21758 The Virtual Linguistics Campus
Artificial Intelligence 40 Semantic Network (Week Slot and Filler Structure) in ai semantic network are alternative to predicate logic in knowledge representation.
Views: 28037 Sanjay Pathak
This introductory E-Lecture, which is part of our series "The Structure of English", discusses the central syntactic functional elements of clause structure in PDE. It serves as an overview, i.e. as a first approach towards a functional analysis of PDE clause structure.
Views: 14059 The Virtual Linguistics Campus
What is the Difference Between Syntax And Semantics In Programming Languages In HINDI : The Syntax of a (programming) language is a set of rules that define what sequences of symbols are considered to be valid expression (programs) in the language. Website: http://www.tutorialsspace.com 21- Boolean Data Types | Scalar Data Type - Boolean in Programming Languages https://youtu.be/elHrH7KXyBU 22- Characters in Programming Languages | Scalar Data Type- Characters in Programming Languages https://youtu.be/bewRNBSEQmY Instagram https://www.instagram.com/tutorialsspace/
Views: 14262 tutorialsspace
https://innoradiant.com/ We help our customers to take to the right decisions in the product development life cycle by identifying user attitudes on social networks. Our help is not in terms of consultancy, but is based on the delivery of VoU, a platform which allows product teams to be completely autonomous in the discovery of “killer features” of the new product. VoU is based on a big data compliant architecture (we do not do much buzz about it, but yes we are dealing with big data!) where several world class Artificial Intelligence libraries have been injected, notably in the domain of Natural Language Processing.
Views: 207 INNORADIANT
Mildred Larson's textbook "Meaning-Based Translation" was not written specifically for ASL-English interpreting, but it's lessons provide a solid basis for being an efficient and effective interpreter. Chapter 3 - Semantic Structure of Language
Views: 7 Troy Terps
HermeneutiX is a tool for analysing the syntactic and semantic structure of texts as part of an exegesis (e.g. biblical exegesis). It is part of the SciToS (scientific tool set) project and freely available on GitHub: https://github.com/scientific-tool-set/scitos/releases ---------------- This video aims at providing a basic tutorial on how to use HermeneutiX and to present an overview of the main features. Contents: 00:00 Introduction 00:48 Download HermeneutiX (SciToS) from GitHub 01:03 Start HermeneutiX (SciToS) from extracted .zip 03:25 Creating a HermeneutiX project & pre-format text 04:48 Performing the syntactic structure analysis 07:22 Performing the semantic structure analysis 08:59 Adding comments and other minor features 12:04 Configuration options (Look & Feel) 13:24 Configuration options (Colors and Fonts in exported SVG files) 13:47 Configuration options (Semantic relations/roles) 14:08 Configuration options (Input Languages, i.e. syntactic functions) 16:27 Exporting to SVG 17:12 How to share configurations ---------------- Additional points: 1. For creating semantic relations over multiple elements (propositions/relations), just tick all of their check boxes and right-click on any one of them to create the relation. Actually it doesn’t matter whether the one you click on has been checked as well. 2. For changing the origin text’s font after starting the analysis, go to „Edit“ – „Edit Project Info“, which includes the origin text font as well as the other meta data (title, author, comment). ---------------- I want to apologize for a few things here: The quality of both video and sound due to my non-professional equipment and lack of experience in creating these screencasts. Since I'm only the (main) developer for HermeneutiX (since 2009) but not the head behind the idea, I've no background in theological studies and are therefore blissfully ignorant to the intricacies of the (biblical) exegesis. --------------- If you have any suggestions how to improve SciToS/HermeneutiX, you're welcome to contact me. Cheers, Carsten
Views: 156 SciToS
In this E-Lecture Prof. Handke discusses several approaches towards the definition of word meaning, among them semantic fiels, componential analysis, meaning postulates and cognitive approaches, such as semantic networks and frames.
Views: 32924 The Virtual Linguistics Campus
A. Lascarides (University of Edinburgh)
Views: 544 Stanford Linguistics
Video shows what semantic means. Of or relating to semantics or the meanings of words.. Reflecting intended structure and meaning.. Petty or trivial; quibbling, niggling.. Semantic Meaning. How to pronounce, definition audio dictionary. How to say semantic. Powered by MaryTTS, Wiktionary
Views: 2487 SDictionary
1.Semantic Function •1.0 Meaning of Vocabulary : Meaning of content words实词 and meaning of functional words虚词 •1.1 Meaning of words interaction: between a content word and a content word; a functional word and a content word. •1.2 In addition to the Content words--Noun/pronoun, Verb and adjective etc , the functional words play an important role in Chinese grammar . They are Preposition, Particles, Adverbs, Conjunctive, Interjection and Onomatopoeia etc. •1.3 A Chinese teacher must pay his attention to and let students know the importance of word collocation in addition to the regular relation of words in a sentence. •1.4 A functional word can produces a grammatical pattern in Chinese . 2.0 •Chinese grammar is dealing with an isolated language which is very weak with morphology and realized by adding words or elements 成分 and arranging of word order 词序. •2.1 •A sentence often contains two kinds of elements A and B. Element A is conveying the basic message that are playing by Nouns, verbs and Adjectives. Element B is conveying the secondary level of messages that are most of time playing by Adverbs and other functional words. •2.2 The above mentioned natural fact in Chinese makes Chinese grammar must takes serious of study on functional words and word order. 4.0 •The "dominant" meaning显性语义 and the "recessive meaning"隐性语义. •4.1 •The "dominant" meaning is the meaning of vocabulary which is clear and easy to get from the word itself. Usually you can find a corresponding meaning from a foreign word. • 4.2 The "recessive meaning is a deeper semantic function. It usually does not come from the
Views: 1171 xiongyingzhanchi
In This Video, we will discuss the Phases of Compiler Design. The Introduction of Lexical Analysis, Syntax Analysis and Semantic Analysis is discussed here.
Views: 18380 GATE Lectures Computer Forum NCR
This video is a sample from my paid program for SLPs, Language Therapy Advance. In this video, I walk you through a way you can help your students define adjectives after doing semantic feature analysis. For more information on the Language Therapy Advance program, go here: https://karen-dudek-brannan.mykajabi.com/store/2VLBETyW
Views: 431 Karen Dudek-Brannan
Follow us on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 223811 Well Academy
What is LATENT SEMANTIC INDEXING? What does LATENT SEMANTIC INDEXING mean? LATENT SEMANTIC INDEXING meaning - LATENT SEMANTIC INDEXING definition - LATENT SEMANTIC INDEXING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Latent semantic indexing (LSI) is an indexing and retrieval method that uses a mathematical technique called singular value decomposition (SVD) to identify patterns in the relationships between the terms and concepts contained in an unstructured collection of text. LSI is based on the principle that words that are used in the same contexts tend to have similar meanings. A key feature of LSI is its ability to extract the conceptual content of a body of text by establishing associations between those terms that occur in similar contexts. LSI is also an application of correspondence analysis, a multivariate statistical technique developed by Jean-Paul Benzécri in the early 1970s, to a contingency table built from word counts in documents. Called Latent Semantic Indexing because of its ability to correlate semantically related terms that are latent in a collection of text, it was first applied to text at Bellcore in the late 1980s. The method, also called latent semantic analysis (LSA), uncovers the underlying latent semantic structure in the usage of words in a body of text and how it can be used to extract the meaning of the text in response to user queries, commonly referred to as concept searches. Queries, or concept searches, against a set of documents that have undergone LSI will return results that are conceptually similar in meaning to the search criteria even if the results don’t share a specific word or words with the search criteria.
Views: 1043 The Audiopedia
Latent semantic analysis is a technique in natural language processing, in particular in vectorial semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA assumes that words that are close in meaning will occur in similar pieces of text. A matrix containing word counts per paragraph is constructed from a large piece of text and a mathematical technique called singular value decomposition is used to reduce the number of rows while preserving the similarity structure among columns. Words are then compared by taking the cosine of the angle between the two vectors formed by any two rows. Values close to 1 represent very similar words while values close to 0 represent very dissimilar words. An information retrieval method using latent semantic structure was patented in 1988 by Scott Deerwester, Susan Dumais, George Furnas, Richard Harshman, Thomas Landauer, Karen Lochbaum and Lynn Streeter. In the context of its application to information retrieval, it is sometimes called Latent Semantic Indexing. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 7260 Audiopedia
Correspondence analysis and ancillary data analysis and visualization methods, such as hierarchical clustering, constitute a powerful and versatile platform for data mining and machine learning. As we describe, semantic analysis of textual data is supported well in this framework, and also analysis of change or anomaly. We begin by taking the prescripts and principles discussed by Robert McKee in his book Story: Substance, Structure, Style, and the Principles of Screenwriting, Methuen, 1999. For McKee, filmscript is the ΓÇ£sensory surfaceΓÇ¥ of the underlying semantics and we show how McKeeΓÇÖs work can be used in the context of the Casablanca movie (and also we refer to the CSI television series). Turning to how this work can be used for collaborative and interactive work, we describe how this data mining platform was used to support collective novel writing in an English creative writing class. As an example of use in social media, we discuss the use of analysis of semantics and change in blog rolls. Finally, for analysis of semantics and change in wider, social environments, we apply the same algorithms to data from 1998-2004 on the terrible Colombian civil violence. REFERENCES F. Murtagh, Correspondence Analysis and Data Coding with R and Java, Chapman and Hall/CRC Press, 2005. F. Murtagh, A. Ganz and J. Reddington, 'New methods of analysis of narrative and semantics in support of interactivity', Entertainment Computing, 2, 115-121, 2011. F. Murtagh, M. Spagat and J.A. Restrepo, 'Ultrametric wavelet regression of multivariate time series: Application to Colombian conflict analysis', IEEE Transactions on Systems, Man, and Cybernetics--Part A: Systems and Humans, 41, 254-263, 2011. F. Murtagh, 'The Correspondence Analysis platform for uncovering deep structure in data and information', Sixth Boole Lecture, Computer Journal, 53 (3), 304-315, 2010. F. Murtagh, A. Ganz, S. McKie, J. Mothe and K. Englmeier, 'Tag clouds for displaying semantics: The case of filmscripts', Information Visualization Journal, 9, 253-262, 2010. F. Murtagh, A. Ganz and S. McKie, 'The structure of narrative: the case of film scripts', Pattern Recognition, 42, 302-312, 2009. (See discussion in Z. Merali, 'Here's looking at you, kid. Software promises to identify blockbuster scripts.', Nature, 453, p. 708, 4 June 2008.)
Views: 49 Microsoft Research
In this talk, we propose a new measure for the semantic similarity of query terms based on the statistical correlation of their frequency functions.┬á We develop an efficient way to approximate this measure using standard dimensionality reduction techniques.┬á This approximation can be computed online to build a data structure with significantly less space and query complexity than a brute-force approach.┬á We use our techniques to analyze data from the MSN query logs, automatically uncovering several interesting similarities.┬á This work has applications in ad auction keyword suggestion tools.
Views: 159 Microsoft Research