Wnspell: a Wordnet-Based Spell Corrector
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Automatic Wordnet Mapping Using Word Sense Disambiguation*
Automatic WordNet mapping using word sense disambiguation* Changki Lee Seo JungYun Geunbae Leer Natural Language Processing Lab Natural Language Processing Lab Dept. of Computer Science and Engineering Dept. of Computer Science Pohang University of Science & Technology Sogang University San 31, Hyoja-Dong, Pohang, 790-784, Korea Sinsu-dong 1, Mapo-gu, Seoul, Korea {leeck,gblee }@postech.ac.kr seojy@ ccs.sogang.ac.kr bilingual Korean-English dictionary. The first Abstract sense of 'gwan-mog' has 'bush' as a translation in English and 'bush' has five synsets in This paper presents the automatic WordNet. Therefore the first sense of construction of a Korean WordNet from 'gwan-mog" has five candidate synsets. pre-existing lexical resources. A set of Somehow we decide a synset {shrub, bush} automatic WSD techniques is described for among five candidate synsets and link the sense linking Korean words collected from a of 'gwan-mog' to this synset. bilingual MRD to English WordNet synsets. As seen from this example, when we link the We will show how individual linking senses of Korean words to WordNet synsets, provided by each WSD method is then there are semantic ambiguities. To remove the combined to produce a Korean WordNet for ambiguities we develop new word sense nouns. disambiguation heuristics and automatic mapping method to construct Korean WordNet based on 1 Introduction the existing English WordNet. There is no doubt on the increasing This paper is organized as follows. In section 2, importance of using wide coverage ontologies we describe multiple heuristics for word sense for NLP tasks especially for information disambiguation for sense linking. -
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 -
Universal Or Variation? Semantic Networks in English and Chinese
Universal or variation? Semantic networks in English and Chinese Understanding the structures of semantic networks can provide great insights into lexico- semantic knowledge representation. Previous work reveals small-world structure in English, the structure that has the following properties: short average path lengths between words and strong local clustering, with a scale-free distribution in which most nodes have few connections while a small number of nodes have many connections1. However, it is not clear whether such semantic network properties hold across human languages. In this study, we investigate the universal structures and cross-linguistic variations by comparing the semantic networks in English and Chinese. Network description To construct the Chinese and the English semantic networks, we used Chinese Open Wordnet2,3 and English WordNet4. The two wordnets have different word forms in Chinese and English but common word meanings. Word meanings are connected not only to word forms, but also to other word meanings if they form relations such as hypernyms and meronyms (Figure 1). 1. Cross-linguistic comparisons Analysis The large-scale structures of the Chinese and the English networks were measured with two key network metrics, small-worldness5 and scale-free distribution6. Results The two networks have similar size and both exhibit small-worldness (Table 1). However, the small-worldness is much greater in the Chinese network (σ = 213.35) than in the English network (σ = 83.15); this difference is primarily due to the higher average clustering coefficient (ACC) of the Chinese network. The scale-free distributions are similar across the two networks, as indicated by ANCOVA, F (1, 48) = 0.84, p = .37. -
Spelling Correction: from Two-Level Morphology to Open Source
Spelling Correction: from two-level morphology to open source Iñaki Alegria, Klara Ceberio, Nerea Ezeiza, Aitor Soroa, Gregorio Hernandez Ixa group. University of the Basque Country / Eleka S.L. 649 P.K. 20080 Donostia. Basque Country. [email protected] Abstract Basque is a highly inflected and agglutinative language (Alegria et al., 1996). Two-level morphology has been applied successfully to this kind of languages and there are two-level based descriptions for very different languages. After doing the morphological description for a language, it is easy to develop a spelling checker/corrector for this language. However, what happens if we want to use the speller in the "free world" (OpenOffice, Mozilla, emacs, LaTeX, ...)? Ispell and similar tools (aspell, hunspell, myspell) are the usual mechanisms for these purposes, but they do not fit the two-level model. In the absence of two-level morphology based mechanisms, an automatic conversion from two-level description to hunspell is described in this paper. previous work and reuse the morphological information. 1. Introduction Two-level morphology (Koskenniemi, 1983; Beesley & Karttunen, 2003) has been applied successfully to the 2. Free software for spelling correction morphological description of highly inflected languages. Unfortunately there are not open source tools for spelling There are two-level based descriptions for very different correction with these features: languages (English, German, Swedish, French, Spanish, • It is standardized in the most of the applications Danish, Norwegian, Finnish, Basque, Russian, Turkish, (OpenOffice, Mozilla, emacs, LaTeX, ...). Arab, Aymara, Swahili, etc.). • It is based on the two-level morphology. After doing the morphological description, it is easy to The spell family of spell checkers (ispell, aspell, myspell) develop a spelling checker/corrector for the language fits the first condition but not the second. -
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. -
Spell Checker
International Journal of Scientific and Research Publications, Volume 5, Issue 4, April 2015 1 ISSN 2250-3153 SPELL CHECKER Vibhakti V. Bhaire, Ashiki A. Jadhav, Pradnya A. Pashte, Mr. Magdum P.G ComputerEngineering, Rajendra Mane College of Engineering and Technology Abstract- Spell Checker project adds spell checking and For handling morphology language-dependent algorithm is an correction functionality to the windows based application by additional step. The spell-checker will need to consider different using autosuggestion technique. It helps the user to reduce typing forms of the same word, such as verbal forms, contractions, work, by identifying any spelling errors and making it easier to possessives, and plurals, even for a lightly inflected language like repeat searches .The main goal of the spell checker is to provide English. For many other languages, such as those featuring unified treatment of various spell correction. Firstly the spell agglutination and more complex declension and conjugation, this checking and correcting problem will be formally describe in part of the process is more complicated. order to provide a better understanding of these task. Spell checker and corrector is either stand-alone application capable of A spell checker carries out the following processes: processing a string of words or a text or as an embedded tool which is part of a larger application such as a word processor. • It scans the text and selects the words contained in it. Various search and replace algorithms are adopted to fit into the • domain of spell checker. Spell checking identifies the words that It then compares each word with a known list of are valid in the language as well as misspelled words in the correctly spelled words (i.e.