Description Distributional semantic word representations have become an integral part in numerous natural language processing pipelines in academia and industry. An open question is how these elementary representations can be composed to capture the meaning of longer units of text. In this talk, I will give an overview of compositional distributional models, their applications and current research directions. Abstract Representing words as vectors in a high-dimensional space has a long history in natural language processing. Recently, neural network based approaches such as word2vec and GloVe have gained a substantial amount of popularity and have become an ubiquituous part in many NLP pipelines for a variety tasks, ranging from sentiment analysis and text classification, to machine translation, recognising textual entailment or parsing. An important research problem is how to best leverage these word representations to form longer units of text such as phrases and full sentences. Proposals range from simple pointwise vector operations, to approaches inspired by formal semantics, deep learning based approaches that learn composition as part of an end-to-end system, and more structured approaches such as anchored packed dependency trees. In this talk I will introduce a variety of compositional distributional models and outline different approaches of how effective meaning representations beyond the word level can successfully be built. I will furthermore provide an overview of the advantages of using compositional distributional approaches, as well as their limitations. Lastly, I will discuss their merit for applications such as aspect oriented sentiment analysis and question answering. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 848 PyData
I demonstrate how to evaluate a distribution for normality using both visual and statistical methods using SPSS.
Views: 398067 how2stats
The four key points are discussed when describing distributions in statistics...Shape, Center, Spread, and Outliers. Please forgive the misspelling of DESCRIBED in the video. TIP to identify Left & Right Skewness: (Thanks LeBadman:) Left: Mean is less than Median is less than Mode Symmetrical: Mean, Median and Mode are approximately equal Right: Mean is greater than Median is greater than Mode You just take: Mean, Median, Mode If it's left skewed, you will see the inequalities pointing to the left. If it's right skewed, you will see the inequalities pointing to the right. Check out http://www.ProfRobBob.com, there you will find my lessons organized by class/subject and then by topics within each class. Find free review test, useful notes and more at http://www.mathplane.com
Views: 95761 ProfRobBob
Katrin Erk: Representing Meaning with a Combination of Logical and Distributional Models Abstract: As the field of Natural Language Processing develops, more ambitious semantic tasks are being addressed, such as Question Answering (QA) and Recognizing Textual Entailment (RTE). Solving these tasks requires (ideally) an in-depth representation of sentence structure as well as expressive and flexible representations at the word level. We have been exploring a combination of logical form with distributional as well as resource-based information at the word level, using Markov Logic Networks (MLNs) to perform probabilistic inference over the resulting representations. In this talk, I will focus on the three main components of a system we have developed for the task of Textual Entailment: (1) Logical representation for processing in MLNs, (2) lexical entailment rule construction by integrating distributional information with existing resources, and (3) probabilistic inference, the problem of solving the resulting MLN inference problems efficiently. I will also comment on how I think the ideas from this system can be adapted to Question Answering and the more general task of in-depth single-document understanding.
What is COLLOSTRUCTIONAL ANALYSIS? What does COLLOSTRUCTIONAL ANALYSIS mean? COLLOSTRUCTIONAL ANALYSIS meaning - COLLOSTRUCTIONAL ANALYSIS definition - COLLOSTRUCTIONAL ANALYSIS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Collostructional analysis is a family of methods developed by (in alphabetical order) Stefan Th. Gries (University of California, Santa Barbara) and Anatol Stefanowitsch (Free University of Berlin). Collostructional analysis aims at measuring the degree of attraction or repulsion that words exhibit to constructions, where the notion of construction has so far been that of Goldberg's construction grammar. Collostructional analysis so far comprises three different methods: collexeme analysis, to measure the degree of attraction/repulsion of a lemma to a slot in one particular construction; distinctive collexeme analysis, to measure the preference of a lemma to one particular construction over another, functionally similar construction; multiple distinctive collexeme analysis extends this approach to more than two alternative constructions; covarying collexeme analysis, to measure the degree of attraction of lemmas in one slot of a construction to lemmas in another slot of the same construction. Collostructional analysis requires frequencies of words and constructions and is similar to a wide variety of collocation statistics. It differs from raw frequency counts by providing not only observed co-occurrence frequencies of words and constructions, but also (1) a comparison of the observed frequency to the one expected by chance; thus, collostructional analysis can distinguish attraction and repulsion of words and constructions; (2) a measure of the strength of the attraction or repulsion; this is usually the log-transformed p-value of a Fisher-Yates exact test. Collostructional analysis differs from most collocation statistics such that (1) it measures not the association of words to words, but of words to syntactic patterns or constructions; thus, it takes syntactic structure more seriously than most collocation-based analyses; (2) it has so far only used the most precise statistics, namely the Fisher-Yates exact test based on the hypergeometric distribution; thus, unlike t-scores, z-scores, chi-square tests etc., the analysis is not based on, and does not violate, any distributional assumptions.
Views: 131 The Audiopedia
Alex. Potamianos talk on " Distributional Semantic Models for Affective Text Analysis and Grammar Induction". Supported by LangTERRA 7FP Project.
Views: 88 "Athena" Research Center
What is PHYLOGEOGRAPHY? What does PHYLOGEOGRAPHY mean? PHYLOGEOGRAPHY meaning -PHYLOGEOGRAPHY definition - PHYLOGEOGRAPHY 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 Phylogeography is the study of the historical processes that may be responsible for the contemporary geographic distributions of individuals. This is accomplished by considering the geographic distribution of individuals in light of genetics, particularly population genetics. This term was introduced to describe geographically structured genetic signals within and among species. An explicit focus on a species' biogeography/biogeographical past sets phylogeography apart from classical population genetics and phylogenetics. Past events that can be inferred include population expansion, population bottlenecks, vicariance and migration. Recently developed approaches integrating coalescent theory or the genealogical history of alleles and distributional information can more accurately address the relative roles of these different historical forces in shaping current patterns. The term phylogeography was first used by John Avise in his 1987 work Intraspecific Phylogeography: The Mitochondrial DNA Bridge Between Population Genetics and Systematics. Historical biogeography addresses how historical geological, climatic and ecological conditions influenced the current distribution of species. As part of historical biogeography, researchers had been evaluating the geographical and evolutionary relationships of organisms years before. Two developments during the 1960s and 1970s were particularly important in laying the groundwork for modern phylogeography; the first was the spread of cladistic thought, and the second was the development of plate tectonics theory. The resulting school of thought was vicariance biogeography, which explained the origin of new lineages through geological events like the drifting apart of continents or the formation of rivers. When a continuous population (or species) is divided by a new river or a new mountain range (i.e., a vicariance event), two populations (or species) are created. Paleogeography, geology and paleoecology are all important fields that supply information that is integrated into phylogeographic analyses. Phylogeography takes a population genetics and phylogenetic perspective on biogeography. In the mid-1970s, population genetic analyses turned to mitochondrial markers. The advent of the polymerase chain reaction (PCR), the process where millions of copies of a DNA segment can be replicated, was crucial in the development of phylogeography. Thanks to this breakthrough, the information contained in mitochondrial DNA sequences was much more accessible. Advances in both laboratory methods (e.g. capillary DNA sequencing technology) that allowed easier sequencing DNA and computational methods that make better use of the data (e.g. employing coalescent theory) have helped improve phylogeographic inference. Early phylogeographic work has recently been criticized for its narrative nature and lack of statistical rigor (i.e. it did not statistically test alternative hypotheses). The only real method was Alan Templeton's Nested Clade Analysis, which made use of an inference key to determine the validity of a given process in explaining the concordance between geographic distance and genetic relatedness. Recent approaches have taken a stronger statistical approach to phylogeography than was done initially.
Views: 365 The Audiopedia
Lecture 2 continues the discussion on the concept of representing words as numeric vectors and popular approaches to designing word vectors. Key phrases: Natural Language Processing. Word Vectors. Singular Value Decomposition. Skip-gram. Continuous Bag of Words (CBOW). Negative Sampling. Hierarchical Softmax. Word2Vec. ------------------------------------------------------------------------------- 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://stanfordonline.stanford.edu/
Views: 283370 Stanford University School of Engineering
*************** Thank for watching. Don't foget subscribe this channel.
Views: 4 101 pete
What is COST-EFFECTIVENESS ANALYSIS? What does COST-EFFECTIVENESS ANALYSIS mean? COST-EFFECTIVENESS ANALYSIS meaning - COST-EFFECTIVENESS ANALYSIS definition - COST-EFFECTIVENESS ANALYSIS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Cost-effectiveness analysis (CEA) is a form of economic analysis that compares the relative costs and outcomes (effects) of different courses of action. Cost-effectiveness analysis is distinct from cost–benefit analysis, which assigns a monetary value to the measure of effect. Cost-effectiveness analysis is often used in the field of health services, where it may be inappropriate to monetize health effect. Typically the CEA is expressed in terms of a ratio where the denominator is a gain in health from a measure (years of life, premature births averted, sight-years gained) and the numerator is the cost associated with the health gain. The most commonly used outcome measure is quality-adjusted life years (QALY). Cost-utility analysis is similar to cost-effectiveness analysis. Cost-effectiveness analyses are often visualized on a plane consisting of four-quadrants, the cost represented on the x–axis and the effectiveness on the y– axis. Cost-effectiveness analysis focuses on maximising the average level of an outcome, distributional cost-effectiveness analysis extends the core methods of CEA to incorporate concerns for the distribution of outcomes as well as their average level and make trade-offs between equity and efficiency, these more sophisticated methods are of particular interest when analysing interventions to tackle health inequality. The concept of cost effectiveness is applied to the planning and management of many types of organized activity. It is widely used in many aspects of life. In the acquisition of military tanks, for example, competing designs are compared not only for purchase price, but also for such factors as their operating radius, top speed, rate of fire, armor protection, and caliber and armor penetration of their guns. If a tank's performance in these areas is equal or even slightly inferior to its competitor, but substantially less expensive and easier to produce, military planners may select it as more cost effective than the competitor. Conversely, if the difference in price is near zero, but the more costly competitor would convey an enormous battlefield advantage through special ammunition, radar fire control and laser range finding, enabling it to destroy enemy tanks accurately at extreme ranges, military planners may choose it instead—based on the same cost effectiveness principle. In the context of pharmacoeconomics, the cost-effectiveness of a therapeutic or preventive intervention is the ratio of the cost of the intervention to a relevant measure of its effect. Cost refers to the resource expended for the intervention, usually measured in monetary terms such as dollars or pounds. The measure of effects depends on the intervention being considered. Examples include the number of people cured of a disease, the mm Hg reduction in diastolic blood pressure and the number of symptom-free days experienced by a patient. The selection of the appropriate effect measure should be based on clinical judgment in the context of the intervention being considered.
Views: 6055 The Audiopedia
Dr. Marco Baroni and Dr. Yoav Goldberg's Lecture on Introduction to Distributional and Neural Methods for Semantics. This lecture was given during a symposium on semantic text processing, focusing on an outlook towards the fast growing industrial activity in this area, which was held at Bar-Ilan University in Novemeber 2014 Bar-Ilan's Website: http://www1.biu.ac.il/indexE.php
Views: 1527 barilanuniversity
✪✪✪✪✪ WORK FROM HOME! Looking for US WORKERS for simple Internet data entry JOBS. $15-20 per hour. SIGN UP here - http://jobs.theaudiopedia.com ✪✪✪✪✪ ✪✪✪✪✪ The Audiopedia Android application, INSTALL NOW - https://play.google.com/store/apps/details?id=com.wTheAudiopedia_8069473 ✪✪✪✪✪ What is POLITICAL ECONOMY? What doe POLITICAL ECONOMY mean? POLITICAL ECONOMY meaning. Political economy is a term used for studying production and trade, and their relations with law, custom, and government, as well as with the distribution of national income and wealth. Political economy originated in moral philosophy. It was developed in the 18th century as the study of the economies of states, or polities, hence the term political economy. In the late 19th century, the term economics came to replace political economy, coinciding with the publication of an influential textbook by Alfred Marshall in 1890. Earlier, William Stanley Jevons, a proponent of mathematical methods applied to the subject, advocated economics for brevity and with the hope of the term becoming "the recognised name of a science." Today, political economy, where it is not used as a synonym for economics, may refer to very different things, including Marxian analysis, applied public-choice approaches emanating from the Chicago school and the Virginia school, or simply the advice given by economists to the government or public on general economic policy or on specific proposals. A rapidly growing mainstream literature from the 1970s has expanded beyond the model of economic policy in which planners maximize utility of a representative individual toward examining how political forces affect the choice of economic policies, especially as to distributional conflicts and political institutions. It is available as an area of study in certain colleges and universities.
Views: 16514 The Audiopedia
Applied Multivariate Analysis by Dr. Amit Mitra,Dr. Sharmishtha Mitra, Department of Mathematics and Science, IIT Kanpur. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 18290 nptelhrd
In this video you will get the content about sampling distribution i.e.,population, sample, parameters and statistics of population and sample, testing of hypothesis, null hypothesis, level of significance, and student-t distribution and its example
Views: 74818 Shahid Ahmed
In this video I explain the basic ideas behind DIStributional COmpositional CATegorical (DisCoCat) models of meaning. These models are used in the Quantum Group (part of the Department of Computer Science, University of Oxford) to study how information flows between words in a sentence to give us the meaning of the sentence as a whole. For more information about this topic, see for example: Coecke, B., Sadrzadeh, M., & Clark, S. (2011). Mathematical Foundations for a Compositional Distributional Model of Meaning. Linguistic Analysis, 36(1–4), 345–384. For more information on how density matrices can model ambiguous words, see: Piedeleu, R., Kartsaklis, D., Coecke, B., & Sadrzadeh, M. (2015). Open System Categorical Quantum Semantics in Natural Language Processing. In Proceedings of the 6th Conference on Algebra and Coalgebra in Computer Science. MinutePhysics has been my great inspiration for making this video, check out the channel here: https://www.youtube.com/user/minutephysics The background music is Deliberate Thought by Kevin MacLeod and it is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) Source: http://incompetech.com/music/royalty-free/?keywords=deliberate+thought Artist: http://incompetech.com/ I also would like to thank Sjoerd Smit for writing a Mathematica script to crop all the images for this video. That has saved me countless hours of work. And of course, thank you, viewer, for watching this video. If you have any comments or funny examples of creative language use that would utterly confuse a computer, please let me know! Maaike Zwart
Views: 1362 DisCoCat
A description of the Dirac delta function as a distribution, its use in integrals, shifted delta functions, delta functions of other functions, derivatives of delta functions, and delta functions in Fourier analysis. Note: there is a mistake in the example integral done on slide 6 around 17:15: the exponentials in the answer should be evaluated at the values of x picked out by the delta functions, i.e. -1 and 1, not x and -x. The answer should be 1 + e/2 + 1/(2*e). Apologies for the confusion this probably caused, and thanks to Manu Kamin and Unni Barchamua for pointing it out. (This lecture is part of a series for a course based on Griffiths' Introduction to Quantum Mechanics. The Full playlist is at http://www.youtube.com/playlist?list=PL65jGfVh1ilueHVVsuCxNXoxrLI3OZAPI.)
Views: 64076 Brant Carlson
In this introductory lecture about constituents, Prof. Handke lists and discusses the main constituent tests and illustrates how they work. This lecture thus constitutes the basis for further work in constituent analysis and should be reconsulted on a regular basis.
Views: 41925 The Virtual Linguistics Campus
Summary of the Saffran, Newport, & Aslin paper appearing in the Journal of Memory & Language in 1996.
Views: 605 Aaron Hamer
Distinguished Lecture Series November 4, 2014 Raymond Mooney: "Deep Natural Language Semantics by Combining Logical and Distributional Methods using Probabilistic Logic" Traditional logical approaches to semantics and newer distributional or vector space approaches have complementary strengths and weaknesses.We have developed methods that integrate logical and distributional models by using a CCG-based parser to produce a detailed logical form for each sentence, and combining the result with soft inference rules derived from distributional semantics that connect the meanings of their component words and phrases. For recognizing textual entailment (RTE) we use Markov Logic Networks (MLNs) to combine these representations, and for Semantic Textual Similarity (STS) we use Probabilistic Soft Logic (PSL). We present experimental results on standard benchmark datasets for these problems and emphasize the advantages of combining logical structure of sentences with statistical knowledge mined from large corpora.
Hi! My name is Andre and this week, we will focus on text classification problem. Although, the methods that we will overview can be applied to text regression as well, but that will be easier to keep in mind text classification problem. And for the example of such problem, we can take sentiment analysis. That is the problem when you have a text of review as an input, and as an output, you have to produce the class of sentiment. For example, it could be two classes like positive and negative. It could be more fine grained like positive, somewhat positive, neutral, somewhat negative, and negative, and so forth. And the example of positive review is the following. "The hotel is really beautiful. Very nice and helpful service at the front desk." So we read that and we understand that is a positive review. As for the negative review, "We had problems to get the Wi-Fi working. The pool area was occupied with young party animals, so the area wasn't fun for us." So, it's easy for us to read this text and to understand whether it has positive or negative sentiment but for computer that is much more difficult. And we'll first start with text preprocessing. And the first thing we have to ask ourselves, is what is text? You can think of text as a sequence, and it can be a sequence of different things. It can be a sequence of characters, that is a very low level representation of text. You can think of it as a sequence of words or maybe more high level features like, phrases like, "I don't really like", that could be a phrase, or a named entity like, the history of museum or the museum of history. And, it could be like bigger chunks like sentences or paragraphs and so forth. Let's start with words and let's denote what word is. It seems natural to think of a text as a sequence of words and you can think of a word as a meaningful sequence of characters. So, it has some meaning and it is usually like,if we take English language for example,it is usually easy to find the boundaries of words because in English we can split upa sentence by spaces or punctuation and all that is left are words.Let's look at the example,Friends, Romans, Countrymen, lend me your ears;so it has commas,it has a semicolon and it has spaces.And if we split them those,then we will get words that are ready for further analysis like Friends,Romans, Countrymen, and so forth.It could be more difficult in German,because in German, there are compound words which are written without spaces at all.And, the longest word that is still in use is the following,you can see it on the slide and it actually stands forinsurance companies which provide legal protection.So for the analysis of this text,it could be beneficial to split that compound word intoseparate words because every one of them actually makes sense.They're just written in such form that they don't have spaces.The Japanese language is a different story.
Views: 3504 Machine Learning TV
Even today, a large part of the most insightful work in corpus linguistics relies on techniques whose use in computer-based corpus studies was pioneered 50 years ago by John Sinclair: collocation and keyword analysis combined with a careful interpretation of the corresponding kwic concordances. Enormous technological advances seem to have had little impact except for allowing corpus linguists to analyze ever larger corpora (even on their own laptop computers) and to make use of automatic linguistic annotation (such as part-of-speech tagging, or the automatic detection of direct speech in novels). At the same time, research in other fields has been transformed fundamentally. Digital humanities applies a wide range of state-of-the-art techniques for data analysis and visualization, providing exciting new perspectives on language that are, however, often far removed from the actual object of study (a divorce often embraced as “distant reading”). In computer science, the age of deep learning has brought advances in artificial intelligence that may have a lasting impact on commerce and industry as well as society: algorithms are claimed to achieve superhuman performance; end-to-end learning translates between dozens of languages without any linguistic knowledge. As a result, the need for human understanding is increasingly questioned. In this talk, guest speaker Professor Stefan Evert discusses perspectives for the future of corpus-linguistic research in such an environment. Rather than uncritically embracing new data analysis techniques or applying deep learning models devoid of any linguistic understanding, he argues that our field needs to develop approaches that combine human interpretation with quantitative analysis and visualization — merging man and machine into what he likes to call, with a little bit of hyperbole, the Hermeneutic Cyborg. Speaker Stefan Evert holds the Chair of Computational Corpus Linguistics at the University of Erlangen-Nuremberg, Germany. After studying mathematics, physics and English linguistics, he received a PhD degree in computa- tional linguistics from the University of Stuttgart, Germany. His research interests include the statistical analysis of corpus frequency data (significance tests in corpus linguistics, statistical association measures, Zipf’s law and word frequency distributions), quantitative approaches to lexical semantics (collocations, multiword expressions and distributional semantics), multidimensional analysis (linguistic variation, language comparison, translation studies), as well as processing large text corpora (IMS Open Corpus Workbench, data model and query language of the Nite XML Toolkit, tools for the Web as corpus).
Views: 384 University of Birmingham
Distributive Law or Partition Law : This law state that a solute distribute itself between two immiscible liquids in a constant ratio of concentration irrespective of its add amount. Click Here To Subscribe My Channel: ➤https://www.youtube.com/channel/UCIKPHuv7Exg6JA8rTNfXghQ?sub_confirmation=1 Click here to Check out my previous video: ➤https://www.youtube.com/watch?v=h-E0gGstCPI ➤https://www.youtube.com/watch?v=rB1eqHdt4w8 ➤https://www.youtube.com/watch?v=qoTd-jpMKAs ➤https://www.youtube.com/watch?v=3e-bbtsUCeo ➤https://www.youtube.com/watch?v=BYakzWtzKtQ ➤https://www.youtube.com/watch?v=DPKwj9vO0Fo ➤https://www.youtube.com/watch?v=CgIzb65ujxE ➤https://www.youtube.com/watch?v=PP8CyPtOfQQ Time Stamp: ➤Start: 0:11 ➤Immiscible Solvents: 1:34 ➤Any Amount: 2:50 ➤Temperature: 4:00 ➤Distribution Constant Kd: 5:00 ➤End : 12:20 Click here to check out my all Playlist: ➤ Mass Spectrometer Full Guid: https://www.youtube.com/playlist?list=PLxVvPOGGkrV4bFhDzFulSd8GINvJq9-oe ➤ Molecular Ion Fuull Lactures: https://www.youtube.com/playlist?list=PLxVvPOGGkrV4SPa5574CBFLT8BTLbKbei ➤ Combustion Analysis Compelete Guide: https://www.youtube.com/playlist?list=PLxVvPOGGkrV7d9X5dlrE_JXWs6fzPZekk ➤ Empirical And Molecular Formulas: https://www.youtube.com/playlist?list=PLxVvPOGGkrV61gcWcyKwior3fRMz-DI13 ➤ Liked videos: https://www.youtube.com/playlist?list=LLIKPHuv7Exg6JA8rTNfXghQ ➤Basic Concepts Full Chapter List:https://www.youtube.com/playlist?list=PLxVvPOGGkrV4y4h8NnhiqaTK3XTTDXmAM Follow Me: ➤ Google +: https://plus.google.com/?utm_source=embedded&utm_medium=googleabout&utm_campaign=link ➤ Youtube: https://www.youtube.com/channel/UCIKPHuv7Exg6JA8rTNfXghQ?sub_confirmation=1 Topic: ➤ Distributive Laws/Partition Law | Chemistry Notes Class 11 | in Hindi/Urdu |Distributive Laws/Partition Law | Chemistry Notes Class 11 | in Hindi/Urdu | Click Here To Subscribe My Channel: ➤https://www.youtube.com/channel/UCIKPHuv7Exg6JA8rTNfXghQ?sub_confirmation=1 ➤➤➤➤➤Thanks For Watching➤➤➤➤➤➤
Views: 14978 Gamer Master Academy
Google Tech Talks May 3, 2007 ABSTRACT Computers have given us a new way of thinking about language. Given a large sample of language, or corpus, and computational tools to process it, we can approach language as physicists approach forces and chemists approach chemicals. This approach is noteworthy for missing out what, from a language-user's point of view, is important about a piece of language: its meaning. I shall present this empiricist approach to the study of language and show how, as we develop accurate tools for lemmatisation, part-of-speech tagging and parsing, we move from the raw input -- a character stream -- to an analysis of that stream in increasingly rich terms: words, lemmas, grammatical structures, Fillmore-style frames. Each step on the journey builds on a large corpus accurately analysed at the previous levels. A distributional thesaurus provides generalisations about lexical behaviour which can then feed into an analysis at the ‘frames' level. The talk will be illustrated with work done within the ‘Sketch Engine' tool. For much NLP and linguistic theory, meaning is a given. Thus formal semantics assumes meanings for words, in order to address questions of how they combine, and WSD (word sense disambiguation) typically takes a set of meanings (as found in a dictionary) as a starting point and sets itself the challenge of identifying which meaning applies. But, since the birth of philosophy, meaning has been problematic. In our approach meaning is an eventual output of the research programme, not an input. Speaker: Adam Kilgarriff Adam Kilgarriff is a research scientist working at the intersection of computational linguistics, corpus linguistics, and dictionary-making. Following a PhD on "Polysemy" from Sussex University, he has worked at Longman Dictionaries, Oxford University Press, and the University of Brighton, and is now Director of two companies, Lexicography MasterClass (http://www.lexmasterclass.com) and Lexical Computing Ltd (http://www.sketchengine.co.uk/) which provide software, training and consultancy in the research areas. Google engEDU Speaker: Adam Kilgarriff
Views: 4099 GoogleTalksArchive
Subscribe today and give the gift of knowledge to yourself or a friend vector space distributional lexical semantics Vector-Space (Distributional) Lexical Semantics. Document corpus. Query String. 1. Doc1 2. Doc2 3. Doc3. Ranked Documents. Information Retrieval System. IR System. The Vector-Space Model. Graphic Representation. Term Weights: Term Frequency. Term Weights: Inverse Document Frequency.... number of slides is : 1 number of slides is : 2 number of slides is : 3 number of slides is : 4 number of slides is : 5 number of slides is : 6 number of slides is : 7 number of slides is : 8 number of slides is : 9 number of slides is : 10 number of slides is : 11 number of slides is : 12 number of slides is : 13 number of slides is : 14 number of slides is : 15 number of slides is : 16 number of slides is : 17 number of slides is : 18 number of slides is : 19 number of slides is : 20 number of slides is : 21 number of slides is : 22 number of slides is : 23 number of slides is : 24 number of slides is : 25
Views: 59 slide king
Description What is the closest word to "king"? Is it "Canute" or is it "crowned"? There are many ways to define "similar words" and "similar texts". Depending on your definition you should choose a word embedding to use. There is a new generation of word embeddings added to Gensim open source NLP package using morphological information and learning-to-rank: Facebook's FastText, VarEmbed and WordRank. Abstract There are many ways to find similar words/docs with an open-source Natural Language processing library Gensim that I maintain. I will give an overview of modern word embeddings like Google's Word2vec, Facebook's FastText, GloVe, WordRank, VarEmbed and discuss what business tasks fit them best. What is the most similar word to "king"? It depends on what you mean by similar. "King" can be interchanged with "Canute", but it's attribute is "crown". We will discuss how to achieve these two kinds of similarity from word embeddings. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 7346 PyData
Talk given at the Workshop on Semantic Spaces at the Intersection of NLP, Physics and Cognitive Science 2016: https://www.sites.google.com/site/semspworkshop Slides: https://sites.google.com/site/semspworkshop/programme Abstract: This paper presents a geometric approach to the problem of modelling the relationship between words and concepts, focusing in particular on analogical phenomena in language and cognition. Grounded in recent theories regarding geometric conceptual spaces, we begin with an analysis of existing static distributional semantic models and move on to an exploration of a dynamic approach to using high dimensional spaces of word meaning to project subspaces where analogies can potentially be solved in an online, contextualised way. The crucial element of this analysis is the positioning of statistics in a geometric environment replete with opportunities for interpretation.
Views: 290 OxfordQuantumVideo
Key notes and summary of Class 8 NCERT Chapter 3 - Mineral and Power Resources. In this chapter we will cover: 1. Minerals, types of minerals, distribution of minerals. 2. Power resources :- conventional resources - like firewood, fossil fuel, coal, petroleum, natural gas, hydel power. non-conventional resources - solar energy, wind energy, nuclear energy, geothermal energy, tydal energy, biogas. --- Click here if you want to subscribe:- https://www.youtube.com/user/TheRealS... --- You can also view playlists of other NCERT Geography videos:- Class 6 - https://www.youtube.com/watch?v=gAZP9... Class 7 - https://www.youtube.com/watch?v=Whz0l... Class 8 - https://www.youtube.com/watch?v=PIrwd... Class 9 - https://www.youtube.com/watch?v=VuDbi... Class 10 - https://www.youtube.com/watch?v=hTT_d... Class 11 - https://www.youtube.com/watch?v=Nntks... Whether you are preparing for UPSC Civil Services Exam, National Defence Academy NDA, Combined Defence Services CDS, Bank (PO, Clerk, Specialist), RBI (PO, Clerk, Specialist), Combined Graduate Level CGL, Central Armed Police Force CAPF (Assistant Commandant), Intelligence Bureau IB and many more exams. You can watch this video without having to read the entire book full of texts. If you find it useful, please like and share. Happy studying!
Views: 103657 Amit Sengupta
Here I play a free BBO tournament with over 4000 entrants.
Views: 715 Peter Hollands
Speaker: Edward Grefenstette (University of Oxford) Title: Concrete sentence spaces Event: Flowin'Cat 2010 (October 2010, University of Oxford) Slides: http://www.cs.ox.ac.uk/quantum/slides/flowincat-edwardgrefenstette.pdf Abstract: Clark, Coecke and Sadrzadeh (2007,2008,2010) have developed a categorical framework relating grammatical analysis to distributional semantic modelling of language meaning, in an effort to incorporate syntactic information into semantic composition operations. They present hand-specified truth-theoretic vectors as examples, leaving it for future work to determine how the semantic vectors used in this framework could be obtained from a corpus. This talk presents a proposed quantitative solution to this problem, showing how concrete semantic spaces can be constructed from a corpus for use within this rich new formalism. This paper was produced based on work completed with Sadrzadeh, Pulman, Clark and Coecke.
Views: 1642 OxfordQuantumVideo
Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 12712 Microsoft Research
Author: Xiaodan Zhu, National Research Council of Canada Abstract: Contrasting meaning is a basic aspect of semantics. Sentiment can be regarded as a special case of it. In this talk, we discuss our deep learning approaches to modeling two basic problems: learning representation for contrasting meaning at the lexical level and performing semantic composition to obtain representation for larger text spans, e.g., phrases and sentences. We first present our neural network models for learning distributed representation that encodes contrasting meaning among words. We discuss how the models utilize both distributional statistics and lexical resources to obtain the state-of-the-art performance on the benchmark dataset, the GRE “most contrasting word” questions. Based on lexical representation, the next basic problem is to learn representation for larger text spans through semantic composition. In the second half of the talk, we focus on deep learning models that learn composition functions by considering both compositional and non-compositional factors. The models can effectively obtain representation for phrases and sentences, and they demonstrate the state-of-the-art performance on different sentiment analysis benchmarks, including the Stanford Sentiment Treebank and the datasets used in SemEval Sentiment Analysis in Twitter. More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 45 KDD2017 video
In a programmatic paper, Jäger and Rosenbach (2008) appeal to the psychological phenomenon of asymmetric priming in order to explain why semantic change in grammaticalization is typically unidirectional, from more concrete and specific meanings towards more abstract and schematic meanings. In this talk, I will re-examine the asymmetric priming hypothesis in the light of experimental and corpus-linguistic evidence. Asymmetric priming is a pattern of cognitive association in which one idea strongly evokes another, while that second idea does not evoke the first one with the same force. For instance, given the word 'paddle', many speakers associate 'water'. The reverse is not true. Given 'water', few speakers associate 'paddle'. Asymmetric priming would elegantly explain why many semantic changes in grammar are unidirectional. For instance, expressions of spatial relations evolve into temporal markers (English be going to), and expressions of possession evolve into markers of completion (the English have‐perfect); the inverse processes are unattested (Heine and Kuteva 2002). The asymmetric priming hypothesis has attracted considerable attention (Chang 2008, Eckardt 2008, Traugott 2008), but as yet, empirical engagement with it has been limited. The experimental results that will be presented rely on reaction time measurements from a maze task (Forster et al. 2009). It was tested whether asymmetric priming obtains between lexical forms and their grammaticalized counterparts, i.e. pairs such as 'keep the light on' (lexical keep) and 'keep reading' (grammatical keep). On the asymmetric priming hypothesis, the former should prime the latter, but not vice versa. We collected data from 200 native speakers of American English via Amazon’s Mechanical Turk platform. All participants were exposed to 40 sentences with different pairs of lexical and grammatical forms (keep, go, have, etc.). Mixed-effects regression modeling (Baayen 2008) was used to assess the impact of priming, lexical/grammatical status, and text frequency on speaker’s reaction times. Contrary to the asymmetric priming hypothesis, the results show a negative priming effect: Speakers who have recently been exposed to lexical keep are significantly slower to process grammatical keep. The second part of the talk will present a corpus-based test of the asymmetric priming hypothesis. The analysis draws on frequency data and distributional semantics. Specifically, token-based semantic vector space modeling (Heylen et al. 2012) is used as a tool that allows us to test whether two subsequent uses of the same linguistic form show systematic asymmetries with regard to their meanings. In the analysis, we observe several priming effects: lexical variants and grammatical variants strongly prime themselves, but lexical forms do not prime their grammatical counterparts. The results suggest that the semantic unidirectionality that is in evidence in many instances of grammatical change is in all likelihood not due to priming.
Views: 394 Martin Hilpert
Dot distribution maps, also known as Dot Maps are used for showing population densities in different regions in a country. Check out products related to Geography, travel and the Outdoors on Amazon: https://www.amazon.com/shop/darrongedgesgeographychannel
Views: 15067 Darron Gedge's Geography Channel
Samory Kpotufe, Princeton University Estimating the mode or modal-sets (i.e. extrema points or surfaces) of an unknown density from sample is a basic problem in data analysis. Such estimation is relevant to other problems such as clustering, outlier detection, or can simply serve to identify low-dimensional structures in high dimensional-data (e.g. point-cloud data from medical-imaging, astronomy, etc). Theoretical work on mode-estimation has largely concentrated on understanding its statistical difficulty, while less attention has been given to implementable procedures. Thus, theoretical estimators, which are often statistically optimal, are for the most part hard to implement. Furthermore for more general modal-sets (general extrema of any dimension and shape) much less is known, although various existing procedures (e.g. for manifold-denoising or density-ridge estimation) have similar practical aim. I’ll present two related contributions of independent interest: (1) practical estimators of modal-sets – based on particular subgraphs of a k-NN graph – which attain minimax-optimal rates under surprisingly general distributional conditions; (2) high-probability finite sample rates for k-NN density estimation which is at the heart of our analysis. Finally, I’ll discuss recent successful work towards the deployment of these modal-sets estimators for various clustering applications. Much of the talk is based on a series of work with collaborators S. Dasgupta, K. Chaudhuri, U. von Luxburg, and Heinrich Jiang.
Views: 215 Center for Brains, Minds and Machines (CBMM)
In this example we look at reading the shape of a distribution. More specifically we look at if it is skewed left, right, or is symmetric. Remember that the skew is the tail of a distribution. For more videos please visit http://www.mysecretmathtutor.com
Views: 96030 MySecretMathTutor
Semantics and grammar, modularity of meaning, Danny Fox (1998, current faculty), Philippe Schlenker (1999) from "50 Years of Linguistics at MIT: a Scientific Reunion" (December 9-11, 2011) http://ling50.mit.edu Video courtesy of Video Visuals
Views: 7990 MITLINGUISTICS
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Views: 756 Artificial Intelligence - All in One
The Natural Language Toolkit (NLTK) is a library of Python that can mine (scrap and upload data) and analyse very large amounts of textual data using computational methods. Sign up for FREE training with University Services: http://research.unimelb.edu.au/infrastructure/research-platform-services/training
Views: 407 Research Bazaar