Unsupervised Normalisation of Historical Spelling
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UAX #44: Unicode Character Database File:///D:/Uniweb-L2/Incoming/08249-Tr44-3D1.Html
UAX #44: Unicode Character Database file:///D:/Uniweb-L2/Incoming/08249-tr44-3d1.html Technical Reports L2/08-249 Working Draft for Proposed Update Unicode Standard Annex #44 UNICODE CHARACTER DATABASE Version Unicode 5.2 draft 1 Authors Mark Davis ([email protected]) and Ken Whistler ([email protected]) Date 2008-7-03 This Version http://www.unicode.org/reports/tr44/tr44-3.html Previous http://www.unicode.org/reports/tr44/tr44-2.html Version Latest Version http://www.unicode.org/reports/tr44/ Revision 3 Summary This annex consolidates information documenting the Unicode Character Database. Status This is a draft document which may be updated, replaced, or superseded by other documents at any time. Publication does not imply endorsement by the Unicode Consortium. This is not a stable document; it is inappropriate to cite this document as other than a work in progress. A Unicode Standard Annex (UAX) forms an integral part of the Unicode Standard, but is published online as a separate document. The Unicode Standard may require conformance to normative content in a Unicode Standard Annex, if so specified in the Conformance chapter of that version of the Unicode Standard. The version number of a UAX document corresponds to the version of the Unicode Standard of which it forms a part. Please submit corrigenda and other comments with the online reporting form [Feedback]. Related information that is useful in understanding this annex is found in Unicode Standard Annex #41, “Common References for Unicode Standard Annexes.” For the latest version of the Unicode Standard, see [Unicode]. For a list of current Unicode Technical Reports, see [Reports]. -
Perfect-Fluid Cylinders and Walls-Sources for the Levi-Civita
Perfect-fluid cylinders and walls—sources for the Levi–Civita space–time Thomas G. Philbin∗ School of Mathematics, Trinity College, Dublin 2, Ireland Abstract The diagonal metric tensor whose components are functions of one spatial coordinate is considered. Einstein’s field equations for a perfect-fluid source are reduced to quadratures once a generating function, equal to the product of two of the metric components, is chosen. The solutions are either static fluid cylinders or walls depending on whether or not one of the spatial coordinates is periodic. Cylinder and wall sources are generated and matched to the vacuum (Levi– Civita) space–time. A match to a cylinder source is achieved for 1 <σ< 1 , − 2 2 where σ is the mass per unit length in the Newtonian limit σ 0, and a match → to a wall source is possible for σ > 1 , this case being without a Newtonian | | 2 limit; the positive (negative) values of σ correspond to a positive (negative) fluid density. The range of σ for which a source has previously been matched to the Levi–Civita metric is 0 σ< 1 for a cylinder source. ≤ 2 1 Introduction arXiv:gr-qc/9512029v2 7 Feb 1996 Although a large number of vacuum solutions of Einstein’s equations are known, the physical interpretation of many (if not most) of them remains unsettled (see, e.g., [1]). As stressed by Bonnor [1], the key to physical interpretation is to ascertain the nature of the sources which produce these vacuum space–times; even in black-hole solutions, where no matter source is needed, an understanding of how a matter distribution gives rise to the black-hole space–time is necessary to judge the physical significance (or lack of it) of the complete analytic extensions of these solutions. -
About the Course • an Introduction and 61 Lessons Review Proper Posture and Hand Position, Home Row Placement, and the Concepts Taught in Typing 1 and 2
About the Course • An introduction and 61 lessons review proper posture and hand position, home row placement, and the concepts taught in Typing 1 and 2. The lessons also increase typing speed and teach the following typing skills: all numbers; tab key; colon, slash, parentheses, symbols, and plus and minus signs; indenting and centering; and capitalization and punctuation rules. • This course uses beautifully designed pages with images of nature for those who are looking for an inexpensive, effective, fun, offline program with a “good and beautiful” feel. The course supports high moral character and gives fun practice—all without the need for flashy and over-stimulating effects that often come with on-screen computer programs. • This course is designed for children who have completed The Good & the Beautiful Typing 2 course or have had the same level of typing experience. Items Needed • Course book and sticker sheet • A laptop or computer with a basic word processing program, such as Word, Pages, or Google Docs • Easel document holder (for standing the course book next to the computer) How the Course Works • The child should complete 1 or more lessons a day, 2–5 days a week. (Lessons take 5–15 minutes, depending on the speed of the child.) The child checks off the shell check box each time a lesson is completed. • To complete a lesson, the child places the course book on an easel document holder next to the laptop or computer and follows the instructions, typing assignments in a basic word processing program. • When a page is completed, the child chooses a sticker and places it on the page where indicated. -
Intelligent Chat Bot
INTELLIGENT CHAT BOT A. Mohamed Rasvi, V.V. Sabareesh, V. Suthajebakumari Computer Science and Engineering, Kamaraj College of Engineering and Technology, India ABSTRACT This paper discusses the workflow of intelligent chat bot powered by various artificial intelligence algorithms. The replies for messages in chats are trained against set of predefined questions and chat messages. These trained data sets are stored in database. Relying on one machine-learning algorithm showed inaccurate performance, so this bot is powered by four different machine-learning algorithms to make a decision. The inference engine pre-processes the received message then matches it against the trained datasets based on the AI algorithms. The AIML provides similar way of replying to a message in online chat bots using simple XML based mechanism but the method of employing AI provides accurate replies than the widely used AIML in the Internet. This Intelligent chat bot can be used to provide assistance for individual like answering simple queries to booking a ticket for a trip and also when trained properly this can be used as a replacement for a teacher which teaches a subject or even to teach programming. Keywords : AIML, Artificial Intelligence, Chat bot, Machine-learning, String Matching. I. INTRODUCTION Social networks are attracting masses and gaining huge momentum. They allow instant messaging and sharing features. Guides and technical help desks provide on demand tech support through chat services or voice call. Queries are taken to technical support team from customer to clear their doubts. But this process needs a dedicated support team to answer user‟s query which is a lot of man power. -
An Ensemble Regression Approach for Ocr Error Correction
AN ENSEMBLE REGRESSION APPROACH FOR OCR ERROR CORRECTION by Jie Mei Submitted in partial fulfillment of the requirements for the degree of Master of Computer Science at Dalhousie University Halifax, Nova Scotia March 2017 © Copyright by Jie Mei, 2017 Table of Contents List of Tables ................................... iv List of Figures .................................. v Abstract ...................................... vi List of Symbols Used .............................. vii Acknowledgements ............................... viii Chapter 1 Introduction .......................... 1 1.1 Problem Statement............................ 1 1.2 Proposed Model .............................. 2 1.3 Contributions ............................... 2 1.4 Outline ................................... 3 Chapter 2 Background ........................... 5 2.1 OCR Procedure .............................. 5 2.2 OCR-Error Characteristics ........................ 6 2.3 Modern Post-Processing Models ..................... 7 Chapter 3 Compositional Correction Frameworks .......... 9 3.1 Noisy Channel ............................... 11 3.1.1 Error Correction Models ..................... 12 3.1.2 Correction Inferences ....................... 13 3.2 Confidence Analysis ............................ 16 3.2.1 Error Correction Models ..................... 16 3.2.2 Correction Inferences ....................... 17 3.3 Framework Comparison ......................... 18 ii Chapter 4 Proposed Model ........................ 21 4.1 Error Detection .............................. 22 -
Second Request for Reconsideration for Refusal to Register SIX-MODE
United States Copyright Office Library of Congress · 101 Independence Avenue SE · Washington, DC 20559-6000 · www.copyright.gov June 12, 2017 Paul Juhasz The Juhasz Law Firm, P.C. 10777 Westheimer, Suite 1100 Houston, TX 77042 Re: Second Request for Reconsideration for Refusal to Register SIX-MODE SIMULATOR, and EIGHT-MODE SIMULATOR; Correspondence ID: 1- lJURlDF; SR 1-3302420841, and 1-3302420241 Dear Mr. Juhasz: The Review Board of the United States Copyright Office (the "Board") considered Amcrest Global Holdings Limited's ("Amcrest") second request for reconsideration of the Registration Program's refusal to register claims in two-dimensional artwork for the works titled "SIX-MODE SIMULATOR" and "EIGHT-MODE SIMULATOR" (the "Works"). After reviewing the applications, deposit copies, and relevant correspondence, along with the arguments in the second request for reconsideration, the Board finds that the Works exhibit copyrightable authorship and thus may be registered. SIX-MODE SIMULATOR is a screen print of a device screen. A clock symbol, a number display, and the battery symbol appear at the top. Below that are six mini screens, each containing a set of roman numerals ( either I and II, or I, II, 111). The mini screens also contain varying arrangements of lines and dots. Below the mini screens are two volume/intensity control symbols (a series of thick dash symbols with the plus and minus signs on either side of the series of dashes), one preceded by the roman numeral I and the other preceded by the roman numeral II. EIGHT-MODE SIMULATOR is also a screen print of a device screen. -
Practice with Python
CSI4108-01 ARTIFICIAL INTELLIGENCE 1 Word Embedding / Text Processing Practice with Python 2018. 5. 11. Lee, Gyeongbok Practice with Python 2 Contents • Word Embedding – Libraries: gensim, fastText – Embedding alignment (with two languages) • Text/Language Processing – POS Tagging with NLTK/koNLPy – Text similarity (jellyfish) Practice with Python 3 Gensim • Open-source vector space modeling and topic modeling toolkit implemented in Python – designed to handle large text collections, using data streaming and efficient incremental algorithms – Usually used to make word vector from corpus • Tutorial is available here: – https://github.com/RaRe-Technologies/gensim/blob/develop/tutorials.md#tutorials – https://rare-technologies.com/word2vec-tutorial/ • Install – pip install gensim Practice with Python 4 Gensim for Word Embedding • Logging • Input Data: list of word’s list – Example: I have a car , I like the cat → – For list of the sentences, you can make this by: Practice with Python 5 Gensim for Word Embedding • If your data is already preprocessed… – One sentence per line, separated by whitespace → LineSentence (just load the file) – Try with this: • http://an.yonsei.ac.kr/corpus/example_corpus.txt From https://radimrehurek.com/gensim/models/word2vec.html Practice with Python 6 Gensim for Word Embedding • If the input is in multiple files or file size is large: – Use custom iterator and yield From https://rare-technologies.com/word2vec-tutorial/ Practice with Python 7 Gensim for Word Embedding • gensim.models.Word2Vec Parameters – min_count: -
NLP - Assignment 2
NLP - Assignment 2 Week 2 December 27th, 2016 1. A 5-gram model is a order Markov Model: (a) Six (b) Five (c) Four (d) Constant Ans : c) Four 2. For the following corpus C1 of 3 sentences, what is the total count of unique bi- grams for which the likelihood will be estimated? Assume we do not perform any pre-processing, and we are using the corpus as given. (i) ice cream tastes better than any other food (ii) ice cream is generally served after the meal (iii) many of us have happy childhood memories linked to ice cream (a) 22 (b) 27 (c) 30 (d) 34 Ans : b) 27 3. Arrange the words \curry, oil and tea" in descending order, based on the frequency of their occurrence in the Google Books n-grams. The Google Books n-gram viewer is available at https://books.google.com/ngrams: (a) tea, oil, curry (c) curry, tea, oil (b) curry, oil, tea (d) oil, tea, curry Ans: d) oil, tea, curry 4. Given a corpus C2, The Maximum Likelihood Estimation (MLE) for the bigram \ice cream" is 0.4 and the count of occurrence of the word \ice" is 310. The likelihood of \ice cream" after applying add-one smoothing is 0:025, for the same corpus C2. What is the vocabulary size of C2: 1 (a) 4390 (b) 4690 (c) 5270 (d) 5550 Ans: b)4690 The Questions from 5 to 10 require you to analyse the data given in the corpus C3, using a programming language of your choice. -
3 Dictionaries and Tolerant Retrieval
Online edition (c)2009 Cambridge UP DRAFT! © April 1, 2009 Cambridge University Press. Feedback welcome. 49 Dictionaries and tolerant 3 retrieval In Chapters 1 and 2 we developed the ideas underlying inverted indexes for handling Boolean and proximity queries. Here, we develop techniques that are robust to typographical errors in the query, as well as alternative spellings. In Section 3.1 we develop data structures that help the search for terms in the vocabulary in an inverted index. In Section 3.2 we study WILDCARD QUERY the idea of a wildcard query: a query such as *a*e*i*o*u*, which seeks doc- uments containing any term that includes all the five vowels in sequence. The * symbol indicates any (possibly empty) string of characters. Users pose such queries to a search engine when they are uncertain about how to spell a query term, or seek documents containing variants of a query term; for in- stance, the query automat* would seek documents containing any of the terms automatic, automation and automated. We then turn to other forms of imprecisely posed queries, focusing on spelling errors in Section 3.3. Users make spelling errors either by accident, or because the term they are searching for (e.g., Herman) has no unambiguous spelling in the collection. We detail a number of techniques for correcting spelling errors in queries, one term at a time as well as for an entire string of query terms. Finally, in Section 3.4 we study a method for seeking vo- cabulary terms that are phonetically close to the query term(s). -
Chapter 13. TABULAR WORK
13. TABULAR WORK (See also ‘‘Abbreviations and Letter Symbols’’; and ‘‘Leaderwork’’) 13.1. The object of a table is to present in a concise and orderly manner information that cannot be presented as clearly in any other way. 13.2. Tabular material should be kept as simple as possible, so that the meaning of the data can be easily grasped by the user. 13.3. Tables shall be set without down (vertical) rules when there is at least an em space between columns, except where: (1) In the judgment of the Government Printing Office down rules are re- quired for clarity; or (2) the agency has indicated on the copy they are to be used. The mere presence of down rules in copy or enclosed sample is not considered a request that down rules be used. The publication dictates the type size used in setting tables. Tabular work in the Congressional Record is set 6 on 7. The balance of con- gressional tabular work sets 7 on 8. Abbreviations 13.4. To avoid burdening tabular text, commonly known abbre- viations are used in tables. Metric and unit-of-measurement abbre- viations are used with figures. 13.5. The names of months (except May, June, and July) when followed by the day are abbreviated. 13.6. The words street, avenue, place, road, square, boulevard, terrace, drive, court, and building, following name or number, are abbreviated. For numbered streets, avenues, etc., figures are used. 13.7. Abbreviate the words United States if preceding the word Government, the name of any Government organization, or as an adjective generally. -
STYLE GUIDE for PLANCK PAPERS
STYLE GUIDE for PLANCK PAPERS C. R. Lawrence, T. J. Pearson, Douglas Scott, L. Spencer, A. Zonca 2013 February 6 Version 2013 February 6 i TABLE OF CONTENTS 1 PURPOSE 1 2 HOW TO REFER TO THE PLANCK PROJECT 1 3 HOW TO REFER TO PLANCK CATALOGUES AND CRYOCOOLERS 1 4 DATES 1 5 ACRONYMS, AND ACTIVE LINKS 2 6 TITLE 2 7 ABSTRACT 2 8 CORRESPONDING AUTHOR AND ACKNOWLEDGMENTS 2 9 UNITS 3 10 NOTATION 4 11 PUNCTUATION, ABBREVIATIONS, AND CAPITALIZATION 4 12 CONTENT 5 13 USE OF THE ENGLISH LANGUAGE 5 14 LaTEX AND TEX; TYPESETTING MATHEMATICS 8 15 REFERENCES 10 16 FIGURES 10 16.1 From the A&A Author’s Guide 10 16.2 Requirements for line plots 11 16.3 Examples of line plots 12 16.4 Requirements for all-sky maps 13 16.5 Requirements for figures with shading 13 16.6 Additional points 13 17 Fixes for two annoying LaTEX problems 16 18 TABLES 16 18.1 Tutorial on Tables 18 18.2 Columns 19 18.3 Centring, etc. in columns 19 18.4 Rules 20 18.5 Space between columns 20 18.6 Space between rows 22 18.7 How to override the template for a particular entry 23 18.8 More on rules 23 18.9 Centring a table 24 18.10 How to make a table of a specified width 24 ii Version 2013 February 6 18.11 How to run something across more than one column 24 18.12 Leaders: filling up a column with rules or dots 25 18.13 How to line up columns of numbers 26 18.14 Adding footnotes to a table 28 18.15 Paragraphs as table entries 30 Version 2013 February 6 4 DATES 1 1 PURPOSE This Style Guide is intended to help authors of Planck-related papers prepare high-quality manu- scripts in a uniform style. -
Use of Word Embedding to Generate Similar Words and Misspellings for Training Purpose in Chatbot Development
USE OF WORD EMBEDDING TO GENERATE SIMILAR WORDS AND MISSPELLINGS FOR TRAINING PURPOSE IN CHATBOT DEVELOPMENT by SANJAY THAPA THESIS Submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at The University of Texas at Arlington December, 2019 Arlington, Texas Supervising Committee: Deokgun Park, Supervising Professor Manfred Huber Vassilis Athitsos Copyright © by Sanjay Thapa 2019 ACKNOWLEDGEMENTS I would like to thank Dr. Deokgun Park for allowing me to work and conduct the research in the Human Data Interaction (HDI) Lab in the College of Engineering at the University of Texas at Arlington. Dr. Park's guidance on the procedure to solve problems using different approaches has helped me to grow personally and intellectually. I am also very thankful to Dr. Manfred Huber and Dr. Vassilis Athitsos for their constant guidance and support in my research. I would like to thank all the members of the HDI Lab for their generosity and company during my time in the lab. I also would like to thank Peace Ossom Williamson, the director of Research Data Services at the library of the University of Texas at Arlington (UTA) for giving me the opportunity to work as a Graduate Research Assistant (GRA) in the dataCAVE. i DEDICATION I would like to dedicate my thesis especially to my mom and dad who have always been very supportive of me with my educational and personal endeavors. Furthermore, my sister and my brother played an indispensable role to provide emotional and other supports during my graduate school and research. ii LIST OF ILLUSTRATIONS Fig: 2.3: Rasa Architecture ……………………………………………………………….7 Fig 2.4: Chatbot Conversation without misspelling and with misspelling error.