Spelling Correction: from Two-Level Morphology to Open Source
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GNU Emacs GNU Emacs
GNU Emacs GNU Emacs Reference Card Reference Card Computing and Information Technology Computing and Information Technology Replacing Text Replacing Text Replace foo with bar M-x replace-string Replace foo with bar M-x replace-string foo bar foo bar Query replacement M-x query replace Query replacement M-x query replace then replace <Space> then replace <Space> then skip to next <Backspace> then skip to next <Backspace> then replace and exit <Period> then replace and exit <Period> then replace all ! then replace all ! then go back ^ then go back ^ then exit query <Esc> then exit query <Esc> Setting a Mark Setting a Mark Set mark here C-<Space> or C-@ Set mark here C-<Space> or C-@ Exchange point and mark C-x C-x Exchange point and mark C-x C-x Mark paragraph M-h Mark paragraph M-h Mark entire file C-x h Mark entire file C-x h Sorting Text Sorting Text Normal sort M-x sort-lines Normal sort M-x sort-lines Reverse sort <Esc>-1 M-x sort lines Reverse sort <Esc>-1 M-x sort lines Inserting Text Inserting Text Scan file fn M-x view-file fn Scan file fn M-x view-file fn Write buffer to file fn M-x write-file fn Write buffer to file fn M-x write-file fn Insert file fn into buffer M-x insert-file fn Insert file fn into buffer M-x insert-file fn Write region to file fn M-x write-region fn Write region to file fn M-x write-region fn Write region to end of file fn M-x append-to-file fn Write region to end of file fn M-x append-to-file fn Write region to beginning of Write region to beginning to specified buffer M-x prepend-to-buffer specified buffer -
Automatic Correction of Real-Word Errors in Spanish Clinical Texts
sensors Article Automatic Correction of Real-Word Errors in Spanish Clinical Texts Daniel Bravo-Candel 1,Jésica López-Hernández 1, José Antonio García-Díaz 1 , Fernando Molina-Molina 2 and Francisco García-Sánchez 1,* 1 Department of Informatics and Systems, Faculty of Computer Science, Campus de Espinardo, University of Murcia, 30100 Murcia, Spain; [email protected] (D.B.-C.); [email protected] (J.L.-H.); [email protected] (J.A.G.-D.) 2 VÓCALI Sistemas Inteligentes S.L., 30100 Murcia, Spain; [email protected] * Correspondence: [email protected]; Tel.: +34-86888-8107 Abstract: Real-word errors are characterized by being actual terms in the dictionary. By providing context, real-word errors are detected. Traditional methods to detect and correct such errors are mostly based on counting the frequency of short word sequences in a corpus. Then, the probability of a word being a real-word error is computed. On the other hand, state-of-the-art approaches make use of deep learning models to learn context by extracting semantic features from text. In this work, a deep learning model were implemented for correcting real-word errors in clinical text. Specifically, a Seq2seq Neural Machine Translation Model mapped erroneous sentences to correct them. For that, different types of error were generated in correct sentences by using rules. Different Seq2seq models were trained and evaluated on two corpora: the Wikicorpus and a collection of three clinical datasets. The medicine corpus was much smaller than the Wikicorpus due to privacy issues when dealing Citation: Bravo-Candel, D.; López-Hernández, J.; García-Díaz, with patient information. -
Emacspeak — the Complete Audio Desktop User Manual
Emacspeak | The Complete Audio Desktop User Manual T. V. Raman Last Updated: 19 November 2016 Copyright c 1994{2016 T. V. Raman. All Rights Reserved. Permission is granted to make and distribute verbatim copies of this manual without charge provided the copyright notice and this permission notice are preserved on all copies. Short Contents Emacspeak :::::::::::::::::::::::::::::::::::::::::::::: 1 1 Copyright ::::::::::::::::::::::::::::::::::::::::::: 2 2 Announcing Emacspeak Manual 2nd Edition As An Open Source Project ::::::::::::::::::::::::::::::::::::::::::::: 3 3 Background :::::::::::::::::::::::::::::::::::::::::: 4 4 Introduction ::::::::::::::::::::::::::::::::::::::::: 6 5 Installation Instructions :::::::::::::::::::::::::::::::: 7 6 Basic Usage. ::::::::::::::::::::::::::::::::::::::::: 9 7 The Emacspeak Audio Desktop. :::::::::::::::::::::::: 19 8 Voice Lock :::::::::::::::::::::::::::::::::::::::::: 22 9 Using Online Help With Emacspeak. :::::::::::::::::::: 24 10 Emacs Packages. ::::::::::::::::::::::::::::::::::::: 26 11 Running Terminal Based Applications. ::::::::::::::::::: 45 12 Emacspeak Commands And Options::::::::::::::::::::: 49 13 Emacspeak Keyboard Commands. :::::::::::::::::::::: 361 14 TTS Servers ::::::::::::::::::::::::::::::::::::::: 362 15 Acknowledgments.::::::::::::::::::::::::::::::::::: 366 16 Concept Index :::::::::::::::::::::::::::::::::::::: 367 17 Key Index ::::::::::::::::::::::::::::::::::::::::: 368 Table of Contents Emacspeak :::::::::::::::::::::::::::::::::::::::::: 1 1 Copyright ::::::::::::::::::::::::::::::::::::::: -
Welsh Language Technology Action Plan Progress Report 2020 Welsh Language Technology Action Plan: Progress Report 2020
Welsh language technology action plan Progress report 2020 Welsh language technology action plan: Progress report 2020 Audience All those interested in ensuring that the Welsh language thrives digitally. Overview This report reviews progress with work packages of the Welsh Government’s Welsh language technology action plan between its October 2018 publication and the end of 2020. The Welsh language technology action plan derives from the Welsh Government’s strategy Cymraeg 2050: A million Welsh speakers (2017). Its aim is to plan technological developments to ensure that the Welsh language can be used in a wide variety of contexts, be that by using voice, keyboard or other means of human-computer interaction. Action required For information. Further information Enquiries about this document should be directed to: Welsh Language Division Welsh Government Cathays Park Cardiff CF10 3NQ e-mail: [email protected] @cymraeg Facebook/Cymraeg Additional copies This document can be accessed from gov.wales Related documents Prosperity for All: the national strategy (2017); Education in Wales: Our national mission, Action plan 2017–21 (2017); Cymraeg 2050: A million Welsh speakers (2017); Cymraeg 2050: A million Welsh speakers, Work programme 2017–21 (2017); Welsh language technology action plan (2018); Welsh-language Technology and Digital Media Action Plan (2013); Technology, Websites and Software: Welsh Language Considerations (Welsh Language Commissioner, 2016) Mae’r ddogfen yma hefyd ar gael yn Gymraeg. This document is also available in Welsh. -
Learning to Read by Spelling Towards Unsupervised Text Recognition
Learning to Read by Spelling Towards Unsupervised Text Recognition Ankush Gupta Andrea Vedaldi Andrew Zisserman Visual Geometry Group Visual Geometry Group Visual Geometry Group University of Oxford University of Oxford University of Oxford [email protected] [email protected] [email protected] tttttttttttttttttttttttttttttttttttttttttttrssssss ttttttt ny nytt nr nrttttttt ny ntt nrttttttt zzzz iterations iterations bcorote to whol th ticunthss tidio tiostolonzzzz trougfht to ferr oy disectins it has dicomered Training brought to view by dissection it was discovered Figure 1: Text recognition from unaligned data. We present a method for recognising text in images without using any labelled data. This is achieved by learning to align the statistics of the predicted text strings, against the statistics of valid text strings sampled from a corpus. The figure above visualises the transcriptions as various characters are learnt through the training iterations. The model firstlearns the concept of {space}, and hence, learns to segment the string into words; followed by common words like {to, it}, and only later learns to correctly map the less frequent characters like {v, w}. The last transcription also corresponds to the ground-truth (punctuations are not modelled). The colour bar on the right indicates the accuracy (darker means higher accuracy). ABSTRACT 1 INTRODUCTION This work presents a method for visual text recognition without read (ri:d) verb • Look at and comprehend the meaning of using any paired supervisory data. We formulate the text recogni- (written or printed matter) by interpreting the characters or tion task as one of aligning the conditional distribution of strings symbols of which it is composed. -
Detecting Personal Life Events from Social Media
Open Research Online The Open University’s repository of research publications and other research outputs Detecting Personal Life Events from Social Media Thesis How to cite: Dickinson, Thomas Kier (2019). Detecting Personal Life Events from Social Media. PhD thesis The Open University. For guidance on citations see FAQs. c 2018 The Author https://creativecommons.org/licenses/by-nc-nd/4.0/ Version: Version of Record Link(s) to article on publisher’s website: http://dx.doi.org/doi:10.21954/ou.ro.00010aa9 Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyright owners. For more information on Open Research Online’s data policy on reuse of materials please consult the policies page. oro.open.ac.uk Detecting Personal Life Events from Social Media a thesis presented by Thomas K. Dickinson to The Department of Science, Technology, Engineering and Mathematics in partial fulfilment of the requirements for the degree of Doctor of Philosophy in the subject of Computer Science The Open University Milton Keynes, England May 2019 Thesis advisor: Professor Harith Alani & Dr Paul Mulholland Thomas K. Dickinson Detecting Personal Life Events from Social Media Abstract Social media has become a dominating force over the past 15 years, with the rise of sites such as Facebook, Instagram, and Twitter. Some of us have been with these sites since the start, posting all about our personal lives and building up a digital identify of ourselves. But within this myriad of posts, what actually matters to us, and what do our digital identities tell people about ourselves? One way that we can start to filter through this data, is to build classifiers that can identify posts about our personal life events, allowing us to start to self reflect on what we share online. -
Emacspeak User's Guide
Emacspeak User's Guide Jennifer Jobst Revision History Revision 1.3 July 24,2002 Revised by: SDS Updated the maintainer of this document to Sharon Snider, corrected links, and converted to HTML Revision 1.2 December 3, 2001 Revised by: JEJ Changed license to GFDL Revision 1.1 November 12, 2001 Revised by: JEJ Revision 1.0 DRAFT October 19, 2001 Revised by: JEJ This document helps Emacspeak users become familiar with Emacs as an audio desktop and provides tutorials on many common tasks and the Emacs applications available to perform those tasks. Emacspeak User's Guide Table of Contents 1. Legal Notice.....................................................................................................................................................1 2. Introduction.....................................................................................................................................................2 2.1. What is Emacspeak?.........................................................................................................................2 2.2. About this tutorial.............................................................................................................................2 3. Before you begin..............................................................................................................................................3 3.1. Getting started with Emacs and Emacspeak.....................................................................................3 3.2. Emacs Command Conventions.........................................................................................................3 -
Easychair Preprint Feedback Learning: Automating the Process
EasyChair Preprint № 992 Feedback Learning: Automating the Process of Correcting and Completing the Extracted Information Rakshith Bymana Ponnappa, Khurram Azeem Hashmi, Syed Saqib Bukhari and Andreas Dengel EasyChair preprints are intended for rapid dissemination of research results and are integrated with the rest of EasyChair. May 12, 2019 Feedback Learning: Automating the Process of Correcting and Completing the Extracted Information Abstract—In recent years, with the increasing usage of digital handwritten or typed information in the digital mailroom media and advancements in deep learning architectures, most systems. The problem arises when these information extraction of the paper-based documents have been revolutionized into systems cannot extract the exact information due to many digital versions. These advancements have helped the state-of-the- art Optical Character Recognition (OCR) and digital mailroom reasons like the source graphical document itself is not readable, technologies become progressively efficient. Commercially, there scanner has a poor result, characters in the document are too already exists end to end systems which use OCR and digital close to each other resulting in problems like reading “Ouery” mailroom technologies for extracting relevant information from instead of “Query”, “8” instead of ”3”, to name a few. It financial documents such as invoices. However, there is plenty of is even more challenging to correct errors in proper nouns room for improvement in terms of automating and correcting post information extracted errors. This paper describes the like names, addresses and also in numerical values such as user-involved, self-correction concept based on the sequence to telephone number, insurance number and so on. -
Hunspell – the Free Spelling Checker
Hunspell – The free spelling checker About Hunspell Hunspell is a spell checker and morphological analyzer library and program designed for languages with rich morphology and complex word compounding or character encoding. Hunspell interfaces: Ispell-like terminal interface using Curses library, Ispell pipe interface, OpenOffice.org UNO module. Main features of Hunspell spell checker and morphological analyzer: - Unicode support (affix rules work only with the first 65535 Unicode characters) - Morphological analysis (in custom item and arrangement style) and stemming - Max. 65535 affix classes and twofold affix stripping (for agglutinative languages, like Azeri, Basque, Estonian, Finnish, Hungarian, Turkish, etc.) - Support complex compoundings (for example, Hungarian and German) - Support language specific features (for example, special casing of Azeri and Turkish dotted i, or German sharp s) - Handle conditional affixes, circumfixes, fogemorphemes, forbidden words, pseudoroots and homonyms. - Free software (LGPL, GPL, MPL tri-license) Usage The src/tools dictionary contains ten executables after compiling (or some of them are in the src/win_api): affixcompress: dictionary generation from large (millions of words) vocabularies analyze: example of spell checking, stemming and morphological analysis chmorph: example of automatic morphological generation and conversion example: example of spell checking and suggestion hunspell: main program for spell checking and others (see manual) hunzip: decompressor of hzip format hzip: compressor of -
Finite State Recognizer and String Similarity Based Spelling
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by BRAC University Institutional Repository FINITE STATE RECOGNIZER AND STRING SIMILARITY BASED SPELLING CHECKER FOR BANGLA Submitted to A Thesis The Department of Computer Science and Engineering of BRAC University by Munshi Asadullah In Partial Fulfillment of the Requirements for the Degree of Bachelor of Science in Computer Science and Engineering Fall 2007 BRAC University, Dhaka, Bangladesh 1 DECLARATION I hereby declare that this thesis is based on the results found by me. Materials of work found by other researcher are mentioned by reference. This thesis, neither in whole nor in part, has been previously submitted for any degree. Signature of Supervisor Signature of Author 2 ACKNOWLEDGEMENTS Special thanks to my supervisor Mumit Khan without whom this work would have been very difficult. Thanks to Zahurul Islam for providing all the support that was required for this work. Also special thanks to the members of CRBLP at BRAC University, who has managed to take out some time from their busy schedule to support, help and give feedback on the implementation of this work. 3 Abstract A crucial figure of merit for a spelling checker is not just whether it can detect misspelled words, but also in how it ranks the suggestions for the word. Spelling checker algorithms using edit distance methods tend to produce a large number of possibilities for misspelled words. We propose an alternative approach to checking the spelling of Bangla text that uses a finite state automaton (FSA) to probabilistically create the suggestion list for a misspelled word. -
Sethesaurus: Wordnet in Software Engineering
This is the author's version of an article that has been published in this journal. Changes were made to this version by the publisher prior to publication. The final version of record is available at http://dx.doi.org/10.1109/TSE.2019.2940439 IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 14, NO. 8, AUGUST 2015 1 SEthesaurus: WordNet in Software Engineering Xiang Chen, Member, IEEE, Chunyang Chen, Member, IEEE, Dun Zhang, and Zhenchang Xing, Member, IEEE, Abstract—Informal discussions on social platforms (e.g., Stack Overflow, CodeProject) have accumulated a large body of programming knowledge in the form of natural language text. Natural language process (NLP) techniques can be utilized to harvest this knowledge base for software engineering tasks. However, consistent vocabulary for a concept is essential to make an effective use of these NLP techniques. Unfortunately, the same concepts are often intentionally or accidentally mentioned in many different morphological forms (such as abbreviations, synonyms and misspellings) in informal discussions. Existing techniques to deal with such morphological forms are either designed for general English or mainly resort to domain-specific lexical rules. A thesaurus, which contains software-specific terms and commonly-used morphological forms, is desirable to perform normalization for software engineering text. However, constructing this thesaurus in a manual way is a challenge task. In this paper, we propose an automatic unsupervised approach to build such a thesaurus. In particular, we first identify software-specific terms by utilizing a software-specific corpus (e.g., Stack Overflow) and a general corpus (e.g., Wikipedia). Then we infer morphological forms of software-specific terms by combining distributed word semantics, domain-specific lexical rules and transformations. -
SDL Tridion Docs Release Notes
SDL Tridion Docs Release Notes SDL Tridion Docs 14 July 2019 ii SDL Tridion Docs Release Notes 1 Welcome to Tridion Docs Release Notes 1 Welcome to Tridion Docs Release Notes This document contains the complete Release Notes for SDL Tridion Docs 14. Customer support To contact Technical Support, connect to the Customer Support Web Portal at https://gateway.sdl.com and log a case for your SDL product. You need an account to log a case. If you do not have an account, contact your company's SDL Support Account Administrator. Acknowledgments SDL products include open source or similar third-party software. 7zip Is a file archiver with a high compression ratio. 7-zip is delivered under the GNU LGPL License. 7zip SFX Modified Module The SFX Modified Module is a plugin for creating self-extracting archives. It is compatible with three compression methods (LZMA, Deflate, PPMd) and provides an extended list of options. Reference website http://7zsfx.info/. Akka Akka is a toolkit and runtime for building highly concurrent, distributed, and fault tolerant event- driven applications on the JVM. Amazon Ion Java Amazon Ion Java is a Java streaming parser/serializer for Ion. It is the reference implementation of the Ion data notation for the Java Platform Standard Edition 8 and above. Amazon SQS Java Messaging Library This Amazon SQS Java Messaging Library holds the Java Message Service compatible classes, that are used for communicating with Amazon Simple Queue Service. ANTLR ANTLR is a powerful parser generator that you can use to read, process, execute, or translate structured text or binary files.