Search results “R svd text mining wikipedia”
Introduction to Text Analytics with R: VSM, LSA, & SVD
This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: - Tokenization, stemming, and n-grams - The bag-of-words and vector space models - Feature engineering for textual data (e.g. cosine similarity between documents) - Feature extraction using singular value decomposition (SVD) - Training classification models using textual data - Evaluating accuracy of the trained classification models Part 7 of this video series includes specific coverage of: - The trade-offs of expanding the text analytics feature space with n-grams. - How bag-of-words representations map to the vector space model (VSM). - Usage of the dot product between document vectors as a proxy for correlation. - Latent semantic analysis (LSA) as a means to address the curse of dimensionality in text analytics. - How LSA is implemented using singular value decomposition (SVD). - Mapping new data into the lower dimensional SVD space. The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/In... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JVc0 See what our past attendees are saying here: https://hubs.ly/H0f5K6Q0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 8979 Data Science Dojo
Information Retrieval WS 17/18, Lecture 10: Latent Semantic Indexing
This is the recording of Lecture 10 from the course "Information Retrieval", held on 9th January 2018 by Prof. Dr. Hannah Bast at the University of Freiburg, Germany. The discussed topics are: Latent Semantic Indexing, Matrix Factorization, Singular Value Decomposition (SVD), Eigenvector Decomposition (EVD). Link to the Wiki of the course: https://ad-wiki.informatik.uni-freiburg.de/teaching/InformationRetrievalWS1718 Link to the homepage of our chair: https://ad.informatik.uni-freiburg.de/
Views: 1114 AD Lectures
Free LSI Keyword Search Tool For Latent Semantic Indexing
Free LSI Keyword Search Tool For Latent Semantic Indexing https://www.youtube.com/watch?v=M4fCKJB6i7E Looking For A Good Keyword Tool? https://www.youtube.com/watch?v=spl0u0iMy0o https://www.youtube.com/watch?v=NdHogIxI0VU https://www.youtube.com/watch?v=ida_Vs3uNZI https://www.youtube.com/watch?v=D6MjLO-tUcQ More Information about Latent Semantic Indexing: Latent semantic analysis - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Latent_semantic_analysisWikipedia Latent semantic analysis - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Latent_semantic_analysis Wikipedia Jump to Benefits of LSI - 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. ‎Overview · ‎Applications · ‎Implementation · ‎Limitations Latent Semantic Indexing - SEO Book www.seobook.com › LSI Latent semantic indexing adds an important step to the document indexing process. In addition to recording which keywords a document contains, the method examines the document collection as a whole, to see which other documents contain some of those same words. How LSI Works - SEO Book www.seobook.com › LSI We mentioned that latent semantic indexing looks at patterns of word distribution (specifically, word co-occurence) across a set of documents. Before we talk ... Latent semantic indexing - The Stanford Natural Language Processing ... nlp.stanford.edu/IR-book/html/.../latent-semantic-indexing-1.html Next: References and further reading Up: Matrix decompositions and latent ... This process is known as latent semantic indexing (generally abbreviated LSI). What Is Latent Semantic Indexing - Search Engine Journal https://www.s Latent semantic analysis - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Latent_semantic_analysis Wikipedia Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of ... ‎Overview · ‎Applications · ‎Implementation · ‎Limitations Semantic analysis (machine learning) - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Semantic_analysis_(machine_lear... Wikipedia In machine learning, semantic analysis of a corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents. Latent semantic analysis (sometimes latent semantic indexing), is a class of ... Probabilistic latent semantic analysis - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_ana... Wikipedia Probabilistic latent semantic analysis (PLSA), also known as probabilistic latent semantic indexing is a statistical technique for the analysis of two-mode and ... Latent Semantic Indexing - SEO Book http://www.seobook.com › LSI Latent semantic indexing adds an important step to the document indexing process. In addition to recording which keywords a document contains, the method ... Latent Semantic Indexing http://c2.com/cgi/wiki?LatentSemanticIndexing Latent semantic indexing adds an important step to the document indexing process. In addition to record People who watched this video: https://youtu.be/M4fCKJB6i7E Also searched online for: Searches related to Latent Semantic Indexing latent semantic indexing tutorial latent semantic indexing seo latent semantic indexing tool latent semantic indexing example latent semantic indexing python latent semantic indexing r latent semantic indexing ppt latent semantic indexing java Don't forget to check out our YouTube Channel: https://www.youtube.com/user/oneclicklearning and click the link below to subscribe to our channel and get informed when we add new content: https://www.youtube.com/user/oneclicklearning -------------------------------------------- #latentsemanticindexingtutorial #latentsemanticindexingseo #latentsemanticindexingtool #latentsemanticindexingexample #latentsemanticindexingpython #latentsemanticindexingr #latentsemanticindexingppt #latentsemanticindexingjava --------------------------------------------
Views: 683 Chet Hastings
What is TOPIC MODEL? What does TOPIC MODEL mean? TOPIC MODEL meaning, definition & explanation
What is TOPIC MODEL? What does TOPIC MODEL mean? TOPIC MODEL meaning - TOPIC MODEL definition - TOPIC MODEL explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, given that a document is about a particular topic, one would expect particular words to appear in the document more or less frequently: "dog" and "bone" will appear more often in documents about dogs, "cat" and "meow" will appear in documents about cats, and "the" and "is" will appear equally in both. A document typically concerns multiple topics in different proportions; thus, in a document that is 10% about cats and 90% about dogs, there would probably be about 9 times more dog words than cat words. The "topics" produced by topic modeling techniques are clusters of similar words. A topic model captures this intuition in a mathematical framework, which allows examining a set of documents and discovering, based on the statistics of the words in each, what the topics might be and what each document's balance of topics is. Topic models are also referred to as probabilistic topic models, which refers to statistic algorithms for discovering the latent semantic structures of an extensive text body. In the age of information, the amount of the written material we encounter each day is simply beyond our processing capacity. Topic models can help to organize and offer insights for us to understand large collections of unstructured text bodies. Originally developed as a text-mining tool, topic models have been used to detect instructive structures in data such as genetic information, images, and networks. They also have applications in other fields such as bioinformatics. Topic models can include context information such as timestamps, authorship information or geographical coordinates associated with documents. Additionally, network information (such as social networks between authors) can be modelled. Approaches for temporal information include Block and Newman's determination the temporal dynamics of topics in the Pennsylvania Gazette during 1728–1800. Grif?ths & Steyvers use topic modeling on abstract from the journal PNAS to identify topics that rose or fell in popularity from 1991 to 2001. Nelson has been analyzing change in topics over time in the Richmond Times-Dispatch to understand social and political changes and continuities in Richmond during the American Civil War. Yang, Torget and Mihalcea applied topic modeling methods to newspapers from 1829–2008. Mimno used topic modelling with 24 journals on classical philology and archaeology spanning 150 years to look at how topics in the journals change over time and how the journals become more different or similar over time. Yin et al. introduced a topic model for geographically distributed documents, where document positions are explained by latent regions which are detected during inference. Chang and Blei included network information between linked documents in the relational topic model, which allows to model links between websites. The author-topic model by Rosen-Zvi et al. models the topics associated with authors of documents to improve the topic detection for documents with authorship information. In practice researchers attempt to fit appropriate model parameters to the data corpus using one of several heuristics for maximum likelihood fit. A recent survey by Blei describes this suite of algorithms. Several groups of researchers starting with Papadimitriou et al. have attempted to design algorithms with probable guarantees. Assuming that the data were actually generated by the model in question, they try to design algorithms that probably find the model that was used to create the data. Techniques used here include singular value decomposition (SVD) and the method of moments. In 2012 an algorithm based upon non-negative matrix factorization (NMF) was introduced that also generalizes to topic models with correlations among topics.
Views: 2645 The Audiopedia
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: 544 The Audiopedia
What is SEMANTIC BOOTSTRAPPING? What does SEMANTIC BOOTSTRAPPING mean? SEMANTIC BOOTSTRAPPING meaning - SEMANTIC BOOTSTRAPPING definition - SEMANTIC BOOTSTRAPPING 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 bootstrapping is a linguistic theory of child language acquisition which proposes that children can acquire the syntax of a language by first learning and recognizing semantic elements and building upon, or bootstrapping from, that knowledge. This theory proposes that children, when acquiring words, will recognize that words label conceptual categories, such as objects or actions. Children will then use these semantic categories as a cue to the syntactic categories, such as nouns and verbs. Having identified particular words as belonging to a syntactic category, they will then look for other correlated properties of those categories, which will allow them to identify how nouns and verbs are expressed in their language. Additionally, children will use perceived conceptual relations, such as Agent of an event, to identify grammatical relations, such as Subject of a sentence. This knowledge, in turn, allows the learner to look for other correlated properties of those grammatical relations. This theory requires two critical assumptions to be true. First, it requires that children are able to perceive the meaning of words and sentences. It does not require that they do so by any particular method, but the child seeking to learn the language must somehow come to associate words with objects and actions in the world. Second, children must know that there is a strong correspondence between semantic categories and syntactic categories. The relationship between semantic and syntactic categories can then be used to iteratively create, test, and refine internal grammar rules until the child's understanding aligns with the language to which they are exposed, allowing for better categorization methods to be deduced as the child obtains more knowledge of the language.
Views: 564 The Audiopedia
TopicNets: Visual Analysis of Large Text Corpora with Topic Modeling
This video demonstrates the features of the TopicNets system with some concrete examples.
Views: 625 brynjargr
Applying Semantic Analyses to Content-based Recommendation and Document Clustering
This talk will present the results of my research on feature generation techniques for unstructured data sources. We apply Probase, a Web-scale knowledge base developed by Microsoft Research Asia, which is generated from the Bing index, search query logs and other sources, to extract concepts from text. We compare the performance of features generated from Probase and two other forms of semantic analysis, Explicit Semantic Analysis using Wikipedia and Latent Dirichlet Allocation. We evaluate the semantic analysis techniques on two tasks, recommendation using Matchbox, which is a platform for probabilistic recommendations from Microsoft Research Cambridge, and clustering using K-Means.
Views: 671 Microsoft Research
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 393595 sentdex
Lecture 3 | GloVe: Global Vectors for Word Representation
Lecture 3 introduces the GloVe model for training word vectors. Then it extends our discussion of word vectors (interchangeably called word embeddings) by seeing how they can be evaluated intrinsically and extrinsically. As we proceed, we discuss the example of word analogies as an intrinsic evaluation technique and how it can be used to tune word embedding techniques. We then discuss training model weights/parameters and word vectors for extrinsic tasks. Lastly we motivate artificial neural networks as a class of models for natural language processing tasks. Key phrases: Global Vectors for Word Representation (GloVe). Intrinsic and extrinsic evaluations. Effect of hyperparameters on analogy evaluation tasks. Correlation of human judgment with word vector distances. Dealing with ambiguity in word using contexts. Window classification. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://online.stanford.edu/
Word2vec with Gensim - Python
This video explains word2vec concepts and also helps implement it in gensim library of python. Word2vec extracts features from text and assigns vector notations for each word. The word relations are preserved using this. A famous result of word2vec is King - Man + Woman = Queen . This concept has lots other applications as well. Gensim is a library in python which is used to create word2vec models for your corpus. We Learn CBOW- Continuous bowl of words and Skip Gram models to get an intuition about word2vec. Download pretrained word2vec models : https://github.com/jhlau/doc2vec Dataset : https://www.kaggle.com/jiriroz/qa-jokes Find the code GitHub: https://github.com/shreyans29/thesemicolon Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon Recommended book for Deep Learning : http://amzn.to/2nXweQS
Views: 58909 The SemiColon
Inverse Problems Lecture 3/2017: deconvolution with truncated SVD, part 2/2
We use truncated Singular Value Decomposition for implementing noise-robust deconvolution. This is a continuation of https://www.youtube.com/watch?v=lCokUeI9aCE&t=117s and https://www.youtube.com/watch?v=yDfMc6-PXmE This is screen capture of Matlab programming I did when teaching my course Inverse Problems at University of Helsinki. The lecture was given at January 25, 2017. Course website: http://wiki.helsinki.fi/display/mathstatKurssit/Inverse+problems%2C+spring+2017 Here is the final code: % Simple illustration of deconvolution in 1D, the method we use is % Truncated Singular Value Decomposition % % Samuli Siltanen January 2017 % Parameters for controlling the plot appearance fsize = 16; lwidth = 2; %% Simulate the measurement % Build a Point Spread Function (PSF) M = 17; psf = ones(1,2*M+1); % This makes sure psf has a unique centre psf = psf/sum(psf); % Normalization of the psf % Construct "unknown signal" f N = 400; x = linspace(0,1,N); % f = zeros(N,1); % f(1:(end/2)) = 1; f = sin(2*pi*x); f = f(:); % Force vector f to be vertical % Construct the convolution matrix A = convmtx(psf,N); A = A(:,(M+1):(end-M)); % Simulate the "measurement" m = A*f; % Simulate "noisy measurement" sigma = .1; mn = A*f + sigma*randn(N,1); %% Compute truncated SVD solution % Determine the SVD of matrix A [U,D,V] = svd(A); svals = diag(D); % Compute reconstruction r_alpha = 15; Dp_alpha = zeros(size(A)); for iii = 1:r_alpha Dp_alpha(iii,iii) = 1/svals(iii); end f0 = V*Dp_alpha*(U.')*m; fn = V*Dp_alpha*(U.')*mn; %% plots % Take a look at the matrix and its singular values figure(2) clf subplot(1,2,1) spy(A) title('Nonzero elements in A','fontsize',fsize) subplot(1,2,2) semilogy(svals,'k.') hold on semilogy(svals(1:r_alpha),'r.') title('Singular values of A (log plot)','fontsize',fsize) % Take a look at few first singular vectors figure(3) clf subplot(5,1,1) plot(V(:,1)) title('Singular vector 1','fontsize',fsize) subplot(5,1,2) plot(V(:,2)) title('Singular vector 2','fontsize',fsize) subplot(5,1,3) plot(V(:,3)) title('Singular vector 3','fontsize',fsize) subplot(5,1,4) plot(V(:,4)) title('Singular vector 4','fontsize',fsize) subplot(5,1,5) plot(V(:,5)) title('Singular vector 5','fontsize',fsize) % Take a look figure(1) clf subplot(3,1,1) plot(f,'k','linewidth',lwidth) hold on plot(mn,'r','linewidth',lwidth) set(gca,'ytick',[0 max(f)],'fontsize',fsize) subplot(3,1,2) plot(f,'k','linewidth',lwidth) hold on plot(f0,'b','linewidth',lwidth) set(gca,'ytick',[0 max(f)],'fontsize',fsize) subplot(3,1,3) plot(f,'k','linewidth',lwidth) hold on plot(fn,'b','linewidth',lwidth) set(gca,'ytick',[0 max(f)],'fontsize',fsize)
Views: 424 Samuli Siltanen
Legendäre Bayern München  wutrede Giovanni Trapattoni
Legendäre Bayern München wutrede Giovanni Trapattoni. zum seinem 77. Geburtstag. seinem 77. Geburtstag. und für Sieg . Bayern Munich München 4 - 2 Juventus
Views: 102995 Neymar barca
sfspark.org: Sandy Ryza, Semantic Indexing of Four Million Documents with Spark
Latent Semantic Analysis (LSA) is a technique in natural language processing and information retrieval that seeks to better understand the latent relationships and concepts in large corpuses. In this talk, we’ll walk through what it looks like to apply LSA to the full set of documents in English Wikipedia, using Apache Spark. Harnessing the Stanford CoreNLP library for lemmatization and MLlib’s scalable SVD implementation for uncovering a lower-dimensional representation of the data, we’ll undertake the modest task of enabling queries against the full extent of human knowledge, based on latent semantic relationships. Sandy Ryza is a senior data scientist at Cloudera focusing on Apache Spark and its ecosystem, and an author of the O’Reilly book Advanced Analytics with Spark. He is a Spark committer and member of the Apache Hadoop project management committee. He graduated Phi Beta Kappa from Brown University. ---------------------------------------------------------------------------------------------------------------------------------------- Scalæ By the Bay 2016 conference http://scala.bythebay.io -- is held on November 11-13, 2016 at Twitter, San Francisco, to share the best practices in building data pipelines with three tracks: * Functional and Type-safe Programming * Reactive Microservices and Streaming Architectures * Data Pipelines for Machine Learning and AI
Views: 642 FunctionalTV
Machine Reading with Word Vectors (ft. Martin Jaggi)
This video discusses how to represent words by vectors, as prescribed by word2vec. It features Martin Jaggi, Assistant Professor of the IC School at EPFL. https://people.epfl.ch/martin.jaggi Tomas Mikolov, Kai Chen, Greg Corrado and Jeffrey Dean (2013). Efficient Estimation of Word Representations in Vector Space. https://arxiv.org/pdf/1301.3781v3.pdf Omar Levy and Yoav Goldberg (2014). Neural Word Embedding as Implicit Matrix Factorization. https://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization.pdf
Views: 17664 ZettaBytes, EPFL
Semantic analysis Meaning
Video is created with the help of wikipedia, if you are looking for accurate, professional translation services and efficient localization you can use Universal Translation Services https://www.universal-translation-services.com?ap_id=ViragGNG Video shows what semantic analysis means. The process of relating syntactic structures, from the levels of phrases, clauses, sentences and paragraphs to the level of the writing as a whole, to their language-independent meanings, removing features specific to particular linguistic and cultural contexts, to the extent that such a project is possible.. The phase in which a compiler adds semantic information to the parse tree and builds the symbol table.. Semantic analysis Meaning. How to pronounce, definition audio dictionary. How to say semantic analysis. Powered by MaryTTS, Wiktionary
Views: 987 ADictionary
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Document retrieval Meaning
Video is created with the help of wikipedia, if you are looking for accurate, professional translation services and efficient localization you can use Universal Translation Services https://www.universal-translation-services.com?ap_id=ViragGNG Video shows what document retrieval means. The matching of a user query against a set of free-text records, including unstructured text, such as newspaper articles, real-estate records or paragraphs in a manual.. document retrieval synonyms: text retrieval. Document retrieval Meaning. How to pronounce, definition audio dictionary. How to say document retrieval. Powered by MaryTTS, Wiktionary
Views: 98 ADictionary
Sniper Ghost Warrior 3 All Weapons Showcase (Primary / Secondary / Sidearm)
Sniper Ghost Warrior 3 All Weapons Showcase (Primary / Secondary / Sidearm) All gameplay recorded with - http://e.lga.to/360gametv This guide shows you all currently available Weapons in Sniper Ghost Warrior 3 as Showcase. Primary Weapons: 00:08 - 01 - Ballance S-AR Metal 00:39 - 02 - XM-2015 01:15 - 03 - Stronskiy 98 01:52 - 04 - Brezatelya 02:31 - 05 - Dragoon SVD 03:05 - 06 - Knight 110 03:40 - 07 - ES-25 04:24 - 08 - Vykop 05:01 - 09 - Archer T-80 05:36 - 10 - BMT 03 06:22 - 11 - Rook SS-97 06:51 - 12 - ACC 50 07:38 - 13 - Shipunov K96 08:11 - 14 - Turret M96 Secondary Weapons: 08:45 - 15 - Archer AR15 09:13 - 16 - AKA-47 09:38 - 17 - Herstal 10:04 - 18 - KT-R 10:30 - 19 - Galeforce Long 10:56 - 20 - FM-3000 UM 11:32 - 21 - OFM 500 12:11 - 22 - Giovanni M4 12:52 - 23 - Origin-12 13:30 - 24 - Takedown Recurve Bow Sidearms: 14:11 - 25 - M1984 Pistol 14:34 - 26 - M1984 Pistol Rail 14:57 - 27 - Garett M9 15:24 - 28 - Herrvalt 99 15:51 - 29 - Wagram 21 16:17 - 30 - Bull 686 16:47 - 31 - SLP .45 17:11 - 32 - SP M23 17:35 - 33 - MP-40 Grad 18:01 - 34 - Sawn-off Shotgun Sniper Ghost Warrior 3 Weapon Locations https://www.youtube.com/playlist?list=PLuGZAFj5iqHfUqC3CUXQfUVdUeacWiQbI Sniper Ghost Warrior 3 Weapon Skins https://www.youtube.com/playlist?list=PLuGZAFj5iqHcmE-ewQHiw5mufXtr13sYV Sniper Ghost Warrior 3 All Guides https://www.youtube.com/playlist?list=PLuGZAFj5iqHeeRu1y0vu4zMqZsle03EgW Support / Donate Paypal: http://bit.ly/1JySiRV Patreon: https://www.patreon.com/360gametv Visit my sites / partner Website: www.360gametv.com Partner: http://e.lga.to/360gametv Twitter: http://twitter.com/360GameTV Subscribe: http://www.youtube.com/subscription_center?add_user=360GameTV Achievements / Trophies: -
Views: 39029 360GameTV
What is AUTOMATED ESSAY SCORING? What does AUTOMATED ESSAY SCORING mean? AUTOMATED ESSAY SCORING meaning - AUTOMATED ESSAY SCORING definition - AUTOMATED ESSAY SCORING 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 Automated essay scoring (AES) is the use of specialized computer programs to assign grades to essays written in an educational setting. It is a method of educational assessment and an application of natural language processing. Its objective is to classify a large set of textual entities into a small number of discrete categories, corresponding to the possible grades—for example, the numbers 1 to 6. Therefore, it can be considered a problem of statistical classification. Several factors have contributed to a growing interest in AES. Among them are cost, accountability, standards, and technology. Rising education costs have led to pressure to hold the educational system accountable for results by imposing standards. The advance of information technology promises to measure educational achievement at reduced cost. The use of AES for high-stakes testing in education has generated significant backlash, with opponents pointing to research that computers cannot yet grade writing accurately and arguing that their use for such purposes promotes teaching writing in reductive ways (i.e. teaching to the test). From the beginning, the basic procedure for AES has been to start with a training set of essays that have been carefully hand-scored. The program evaluates surface features of the text of each essay, such as the total number of words, the number of subordinate clauses, or the ratio of uppercase to lowercase letters - quantities that can be measured without any human insight. It then constructs a mathematical model that relates these quantities to the scores that the essays received. The same model is then applied to calculate scores of new essays. Recently, one such mathematical model was created by Isaac Persing and Vincent Ng. which not only evaluates essays on the above features, but also on their argument strength. It evaluates various features of the essay, such as the agreement level of the author and reasons for the same, adherence to the prompt's topic, locations of argument components (major claim, claim, premise), errors in the arguments, cohesion in the arguments among various other features. In contrast to the other models mentioned above, this model is closer in duplicating human insight while grading essays. The various AES programs differ in what specific surface features they measure, how many essays are required in the training set, and most significantly in the mathematical modeling technique. Early attempts used linear regression. Modern systems may use linear regression or other machine learning techniques often in combination with other statistical techniques such as latent semantic analysis and Bayesian inference. Any method of assessment must be judged on validity, fairness, and reliability. An instrument is valid if it actually measures the trait that it purports to measure. It is fair if it does not, in effect, penalize or privilege any one class of people. It is reliable if its outcome is repeatable, even when irrelevant external factors are altered. Before computers entered the picture, high-stakes essays were typically given scores by two trained human raters. If the scores differed by more than one point, a third, more experienced rater would settle the disagreement. In this system, there is an easy way to measure reliability: by inter-rater agreement. If raters do not consistently agree within one point, their training may be at fault. If a rater consistently disagrees with whichever other raters look at the same essays, that rater probably needs more training. Various statistics have been proposed to measure inter-rater agreement. Among them are percent agreement, Scott's ?, Cohen's ?, Krippendorf's ?, Pearson's correlation coefficient r, Spearman's rank correlation coefficient ?, and Lin's concordance correlation coefficient. Percent agreement is a simple statistic applicable to grading scales with scores from 1 to n, where usually 4 ? n ? 6. It is reported as three figures, each a percent of the total number of essays scored: exact agreement (the two raters gave the essay the same score), adjacent agreement (the raters differed by at most one point; this includes exact agreement), and extreme disagreement (the raters differed by more than two points). Expert human graders were found to achieve exact agreement on 53% to 81% of all essays, and adjacent agreement on 97% to 100%.....
Views: 353 The Audiopedia
What Is Semantic Analysis In Compiler Design?
▻ Synthesis of a machine language program. Iitmbasics of semantic analysis phase. Semantics of a language provide meaning to its constructs, like tokens and syntax structure. Palabra clave, end)semantic analysis. Introduction semantic analysis in compilers with 2 o so there may be conflicts the design of some analysers, which might analysis, to obtain an adequate compiler respect power and efficiency 27 2016 (lexical parsing) back end. Principles of compiler design nptel. Compiler design syntactic and semantic analysis reinhard the compiler so far. In compiler development, design is king. Compiler design semantic analysis tutorialspoint. A simple compiler part 4 semantic analysis the symbol table. Compiler design lecture semantic analysis, various phases of compiler syntactic and analysis. Convention is that syntax what can be specified by cfg. An overview of a compilerlexical analysis part 1 · Lexical 2 simple compiler 4 semantic the symbol tablea review before we get started this time, let us what have covered so far. Intermediate representations a syntax tree (and no full parse tree), and semantic analysis is done over separate traversal of static. 13 lessons, ( palabra clave, output)( id, a). Reporting compile time errors in the code (except syntactic errors, which are caught by analysis); Generating object (e. We need to ensure the program is a large part of semantic analysis consists tracking variable function type final requirement for designing checking system listing rules in this lesson, educator explains relationship phase with syntax and intermediate code generation. ▫ Scoping (readings 7. Understanding and perceiving semantic analysis enrique chapter 4 analysissyntax directed translation, compiler design. Nptel &middotprinciples of compiler design (video); An overview a compileran. ▻ Analysis of the source program. 6)&#9643▫ Detects inputs with illegal tokens course available from 16 october 2014. Cs3300 compiler design intro to semantic analysis cse. Htm url? Q webcache. Semantic routines interpret meaning of the program based on its syntactic structure two purposes finish analysis by deriving context sensitive information (e. &#9643&#9643▫ Parameter passing methods (7. In practice, anything that requires compiler to 25 nov 2013 check source program for semantic errors. Nthu), and fischer, leblanc▻ Any compiler must perform two major tasks. If you design your language before start coding, you'll have a much better chance at successSemantic analysis (compilers) wikipedia. Googleusercontent search. Reinhard compiler design semantic analysis wisdom jobs. Language design is where you make the decisions that drive what can be statically checked 8 nov 2012 compiler and constructionslides modified from louden book, dr. Free shipping on qualifying offers. Semantics help interpret symbols, their types, and relations with each other. Collect type information for code generation. Doesn't match intuiti
Views: 111 Vernie Liefer Tipz
What Is In LSA?
When it comes to lsa, a little goes long way bettering your health. Lsa or lsa is an initialism standing for in law[edit]. Lsa stands for a blend of ground linseeds (flax seeds), sunflower seeds, and almonds feb 11, 2017 lsa linseeds, seeds. Lsa stands for linseeds, sunflower seeds, and almonds. It was later discovered to be natural, and is known as a you too can experience the benefits of lsa, even without having go through liver cleansing diet lsa ground mixture linseeds, sunflower seeds almonds. Science and the link state advertisement (lsa) is a basic communication means of ospf routing protocol for internet (ip). Ten foods you should eat this year body soulthe benefits of lsa energy fields health foodhealthy food guide. List of cfr sections affected, a list new revisions to the us code federal regulations. Lsa meal nutrition information eat this muchwhat is lsa? Youtubelink state advertisement wikipedia. Latent semantic analysis (lsa) is a theory and method for extracting representing the jun 29, 2014 lsa was first produced by dr sandra cabot discussed in her book liver cleansing diet featured below amazon section 15, 2015 this week our classic foodies will be crumbing chicken breasts (ground linseeds, sunflower seeds, almonds) which yummy alternative to breadcrumbs. A packet of lsa is a combination these three pre mixed seeds, which have been ground down to fine or coarse form jan 7, 2012 lsa, made from linseeds, sunflower seeds and almonds, an easy, extremely versatile way add extra nutrients meals. Flax seed, sunflower almond mixture flax. Mix 1 2 tablespoons of lsa to meals or which is available for downloading on the group papers page. Wanting to know what is lsa? Then find out here exactly it is, the benefits of lsa as well how use. Great for vegetarian, vegan and meat eater as it feb 13, 2012. The what are lsa foods and why should i eat them? Beautyheaven. Lsa, a combination of ground linseeds, sunflower seeds and almonds, has become popular addition to many diets over jan 29, 2017 enjoy the benefits nuts in an healthy lsa mixture. What does home lsa in bsnl means? Quora. It communicates the router's local lsa(local service area) is area where you won't come under roaming. Some different depending on the dosage lsa can produce hallucinations similar to a small of lsd or shoorms mushrooms that contain psilcybin, psilocin, Flax seed, sunflower almond mixture flax. Linseed, sunflower view the nutrition for lsa meal, including calories, carbs, fat, protein, cholesterol, and more (linseed (50. Lsa is rich lsa, d lysergic acid amide or ergine, a product during the creation of lsd, and psychoactive in itself. Lsa is readily available in health food stores and can now be found most supermarkets nov 20, 2012 read about lsa, what it how you incorporate into your diet. The lsa lowdown lose baby weightwhat is lsa? Latent semantic analysis. Its versatile nature lends itself to almost anything. Lsa) mix lsa linseed, sunflower, almond benefits of nu
Views: 83 Question Bag
Algebraic Techniques for Multilingual Document Clustering
Google Tech Talks January 25, 2011 Presented by Brett Bader. ABSTRACT Multilingual documents pose difficulties for clustering by topic, not least because translating everything to a common language is not feasible with a large corpus or many languages. This presentation will address those difficulties with a variety of novel algebraic methods for efficiently clustering multilingual text documents, and brieflyillustrate their implementation via high performance computing. The methods use a multilingual parallel corpus as a 'Rosetta Stone' from which algorithmic variations (including statistical morphological analysis to bypass the need for stemming) of Latent Semantic Analysis (LSA) are able to learn concepts in term space. New documents are projected into this concept space to produce language-independent feature vectors for subsequent use in similarity calculations or machine learning applications. Our experiments show that the new methods have better performance than LSA, and possess some interesting and counter-intuitive properties. Brett W. Bader received his Ph.D. in computer science from the University of Colorado at Boulder, studying higher-order methods for optimization and solving systems of nonlinear equations. In 2003, Brett received the John von Neumann Research Fellowship at Sandia National Laboratories, where he now develops algorithms for multi-way data analysis and machine learning for informatics applications in networks and text.
Views: 3396 GoogleTechTalks
SCP-701 The Hanged King's Tragedy | Euclid | Humanoid scp
SCP-701, The Hanged King's Tragedy, is a Caroline-era revenge tragedy in five acts. Performances of the play are associated with sudden psychotic and suicidal behavior among both observers and participants, as well as the manifestation of a mysterious figure, classified as SCP-701-1. Historical estimates place the number of lives claimed by the play at between █████ and █████ over the past three hundred years. Read along with me! ♣Read along: http://scp-wiki.wikidot.com/scp-701 http://scp-wiki.wikidot.com/scp7011640b1 http://scp-wiki.wikidot.com/incident-report-scp70119971 Help me out on Patreon! ▼Patreon▼ https://www.patreon.com/EastsideShowSCP Join me on Facebook and Twitter! ♣Facebook: https://www.facebook.com/EastsideShowscp ♣Twitter: https://twitter.com/Eastsideshowscp "Long note One" Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 3.0 License http://creativecommons.org/licenses/by/3.0/ Other ♣Music by Kevin MacLeod: http://incompetech.com/ ♥Be sure to like, comment, share, and subscribe!♥
Views: 34624 The Eastside Show
2017 World Championships | Tomokazu Harimoto Interview
Check out what 13-year-old Tomokazu Harimoto has to share about his unbelievable upset over Rio 2016 bronze medalist Jun Mizutani on his World Championships debut! Subscribe here for more official Table Tennis highlights: http://bit.ly/ittfchannel. ©ITTF All content is the copyright of the International Table Tennis Federation. Images may not be reproduced without prior approval from the ITTF.
Views: 34014 Official ITTF Channel
Mod-01 Lec-32 Word Sense Disambiguation
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 2371 nptelhrd
Top 15 Mysteries Solved by 4Chan
► Narrated by Chills: http://bit.ly/ChillsYouTube Follow Top15s on Twitter: http://bit.ly/Top15sTwitter Follow Chills on Instagram: http://bit.ly/ChillsInstagram Follow Chills on Twitter: http://bit.ly/ChillsTwitter Subscribe to Chills on Reddit: http://bitly.com/ChillsReddit In this top 15 list, we were looking at unsolved mysteries that were solved and explained by the popular website, 4Chan. These entries range from strange to creepy. Enjoy our analysis of them. Written by: jessicaholom Edited by: Huba Áron Csapó Sources: https://pastebin.com/c4x7BwE8 Music: Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 3.0 http://creativecommons.org/licenses/by/3.0 Update (Jan. 28, 2018): This is the video has become the "Burger King Foot Lettuce" meme!
Views: 5584748 Top15s
BE DEUTSCH! [Achtung! Germans on the rise!] | NEO MAGAZIN ROYALE mit Jan Böhmermann - ZDFneo
English description below Die Welt dreht durch! Europa fühlt sich so schwach, dass es sich von 0,3% Flüchtlingen bedroht sieht, Amerika ist drauf und dran einen Mann zu wählen, bei dem niemand so genau weiß, wer unter dem Toupet die Fäden zieht und als wäre das alles noch nicht schlimm genug, muss man sich nun auch noch ausgerechnet von Deutschland darüber belehren lassen, wie man sich moralisch richtig verhält. Ausgerechnet Deutschland! Die haben noch nicht mal einen Weltkrieg gewonnen! The world is going completely nuts! Europe feels threatened by 0.3% refugees, the USA are about to elect a man, of who no one really knows who is pulling the strings under the toupee and just as if that was not bad enough, Germany of all nations has to disabuse the world of how to behave morally right. I mean GERMANY! They did not even win one single world war in history!
Mod-01 Lec-26 NLP and IR: How NLP has used IR, Toward Latent Semantic
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 1635 nptelhrd
ARD-Wahlarena: Frage an Merkel zur Pflege
Ein Pflege-Auszubildender beklagt die Zustände in seinem Berufsfeld: Azubi Alexander Jorde fragt Bundeskanzlerin Angela Merkel, warum die Krankenpflege hierzulande in einem so schlechten Zustand ist. Mehr Informationen zum Thema gibt es auch hier: http://www.tagesschau.de/inland/btw17/krankenpflege-105.html Abonnieren Sie hier unseren Kanal: http://bit.ly/2m7yCUu
Views: 340792 tagesschau
Aurora Flight Sciences' Electric VTOL Aircraft
Uber has selected Aurora Flight Sciences as a partner to develop electric vertical takeoff and landing (eVTOL) aircraft for its Uber Elevate Network. Aurora’s eVTOL concept is derived from its XV-24A X-plane program currently underway for the U.S. Department of Defense and other autonomous aircraft the company has developed over the years.
Views: 159193 Aurora Flight Sciences
Nintendo SNES Mini vs. SNES Original / Graphics comparison
Wie gut schlägt sich der Nintendo SNES Mini eigentlich in Sachen Bildqualität im Vergleich zur originalen Konsole? Wir haben die originale Konsole angeschlossen und zeigen euch in diesem Grafikvergleich anhand verschiedener Spiele die Unterschiede! ▶ PC Games abonnieren: http://bit.ly/1gR7TSo Mehr von PC Games: ▶ PC Games Webseite: http://www.pcgames.de ▶ PC Games bei Facebook: http://www.facebook.com/pcgames.de ▶ PC Games auf Twitter: http://twitter.com/pcg_de ▶ PC Games auf Instagram: http://to.pcgames.de/avJ0t #pcg #pcgames
Views: 187585 PC Games
Tomorrow belongs to me   Cabaret
A famous song from Cabaret movie (1972) with Liza Minelli, Joel Gray and Michael York. The rise of Nazi-party (NSDAP) in early 1930s in Germany is shown very good in the movie.
Views: 2262539 Peter Krasnopyorov
Comeback? "Ich bin sehr positiv!"✌ - Manuel Neuer im Interview | FC Bayern.tv live
Eure Fragen - Manuels Antworten: FC Bayern-Torhüter Manuel Neuer ist zu Gast in der Fan Show bei FC Bayern.tv live. Wenn ihr erfahren möchtet, wie die Reha für unseren Kapitän läuft und wann er sein Comeback geben möchte, schaut rein! 🔝 ► Abonnieren/Subscribe: http://fcb.de/youtube Facebook: https://www.facebook.com/FCBayern Twitter: https://twitter.com/fcbayern Instagram: http://www.instagram.com/fcbayern Snapchat: http://fcb.de/FCBayernSnaps Website: https://fcbayern.com FC Bayern.tv: https://fcbayern.com/fcbayerntv FC Bayern.tv live: https://fcbayern.com/fcbayerntv/de/fcbayerntvlive
Views: 63316 FC Bayern München
Engelland delivers emotional speech before puck drop
Vegas Golden Knights' Deryk Engelland addresses the crowd at T-Mobile Area to encourage the feeling of Vegas Strong ahead of the Knight' first game in their home arena.
Views: 61241 SPORTSNET
Jimmie Åkesson - Snart är det val
Vårt land mår inte bra. Socialdemokraterna och Moderaterna gör allt vad de kan för att släta över de problem som deras egna politik skapat för vårt samhälle. Deras ansvarslösa agerande, deras ovilja att utvärdera resultaten av deras politik, och framförallt deras lögner, har skapat det samhälle som vi alla tvingas leva i idag. Just nu pågår förberedelserna inför vår största valkampanj någonsin. Om mindre än ett år är det val!
Views: 1021461 Sverigedemokraterna
Legazpi, Albay | Wikipedia audio article
This is an audio version of the Wikipedia Article: Legazpi, Albay Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. You can find other Wikipedia audio articles too at: https://www.youtube.com/channel/UCuKfABj2eGyjH3ntPxp4YeQ In case you don't find one that you were looking for, put a comment. This video uses Google TTS en-US-Standard-D voice. SUMMARY ======= Legazpi, officially the City of Legazpi, (Central Bicolano: Ciudad kan Legazpi; Filipino: Lungsod ng Legazpi; Spanish: Ciudad de Legazpi ) and often referred to as Legazpi City, is a component city and the capital of the province of Albay in the Philippines. According to the 2015 census, it has a population of 196,639. Legazpi is the regional center and largest city of the Bicol Region, in terms of population. It is the region's center of tourism, education, health services, commerce and transportation in the Bicol Region. The city is composed of two districts: Legazpi Port and Old Albay District. Mayon Volcano, one of the Philippines' most popular icons and tourist destinations, is partly within the city's borders.In 2018, Legazpi was ranked first in overall competitiveness among component cities by the National Competitiveness Council. The city also ranked first in infrastructure and second in economic dynamism. In the same year, Legazpi was also named "most business-friendly city" in the component city category by the Philippine Chamber of Commerce and Industry.
Views: 2 wikipedia tts
Hurensöhne Mannheims - Album Trailer | NEO MAGAZIN ROYALE mit Jan Böhmermann - ZDFneo
Vor zwei Wochen veröffentlichte Xavier Naidoo gemeinsam mit den Söhnen Mannheims das politisch korrekteste Album des Jahres. Entgegen den bösen Behauptungen der Presse, konnten wir absolut keine fremdenfeindlichen Texte auf “Mannheim” finden. Wer nach den 16 sehr guten politischen Werken Xaviers noch nicht genug hat, kann sich jetzt besonders freuen: Ab sofort ist nämlich sein bisher ehrlichstes Album mit den Hurensöhnen Mannheims im ganzen Reich erhältlich. “Death to Israel” ist garantiert nicht antisemitisch und 100%ig frei von Fremdenfeindlichkeit! Ein Album voller gefühlvoller Politballaden, mit denen jeden Tag Montagsdemo in eurem CD-Player ist. Das Neo Magazin Royale - jeden Donnerstag ab 20:15 auf http://neomagazinroyale.de, um 22:15 Uhr in ZDFneo und freitags sehr spät im ZDF.
class 8 chapter 1 science  NCERT crop management
class 8 chapter 1 science NCERT crop management
Views: 30631 Ncert Tutorial
Supergirl XXX Trailer
http://www.comicbookmovie.com From the director who brought you "The Justice League of Porn Star Heoes", comes the latest Sinister X production - "Supergirl XXX: An Extreme Comixxx Parody". Earth's sexiest super hero is a young co-ed from another planet. Supergirl (Alanah Rae) barely escapes the destruction of her home planet, but her greatest challenge lies ahead--sorority life! With the help of her cousin, Superman (Dale Dabone) and her bff, Barbara Gordon (Sunny Lane), Supergirl tries to pass off as human. The only thing standing in her way from a life of text books, keg parties and casual sex, is Natasha Luthor (Andy San Dimas), the Queen Bitch of the Tri-Pi Sorority. In Luthor's plan for world domination, Supergirl is the last piece of the puzzle.
Views: 1131831 ComicBookMovie
FanBox Video Earnings
You dont have to write nothing. all you do is find interesting blogs on the web that you think others will real or view. thats it waaalaaaa. u make money . the more someone views the post or blog you make money, copy and paste a blog or post you find on the web and paste it on fanbox. share it and waalaaa u make money. look at my articles, posts, blogs. there are picture blogs u can make money also. find 20 pictures and make a post. create a free article on the pictures and waalaaa. hope this helped http://www.fanboxrocks.info/ Hi there Fan, pm me for tips ok hun. start posting & createing post using poems they really get more hits, view an ratings. Also rate postings daily for points for promoting your own articles an blogs. Here's where you rate postings, bookmark it: http://blogs.fanbox.com/GenieGoals.aspx?mode=candr&source=geniewarmup Everytime you get a little extra money in your matured area to cash out. Dont cash out. Use that to promote your articles and blogs for the next months payout. Here is my profile stuff: http://posts.fanbox.com/m46n4 And heres a Fanbox Earning Plan that might help you: http://posts.fanbox.com/m56n4 Keep posting and work daily rating & commenting on others postings. It all makes you money. you dont have to pay for anything, its just posting pictures and copying and pasteing blog, and yes you make money. Fanbox Rocks was here to Say You Rock! Keep Up The Articles and start Posting Have a swell day Friend ty hun
Views: 38861 skyewardz

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