Spell Checker in CET Designer
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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. -
NLP Commercialisation in the Last 25 Years
Natural Language Engineering (2019), 25, pp. 419–426 doi:10.1017/S1351324919000135 Anniversary INDUSTRY WATCH NLP commercialisation in the last 25 years Robert Dale∗ Language Technology Group ∗Corresponding author. Email: [email protected] Abstract The Journal of Natural Language Engineering is now in its 25th year. The editorial preface to the first issue emphasised that the focus of the journal was to be on the practical application of natural language processing (NLP) technologies: the time was ripe for a serious publication that helped encourage research ideas to find their way into real products. The commercialisation of NLP technologies had already started by that point, but things have advanced tremendously over the last quarter-century. So, to celebrate the journal’s anniversary, we look at how commercial NLP products have developed over the last 25 years. 1. Some context For many researchers, work in natural language processing (NLP) has a dual appeal. On the one hand, the computational modelling of language understanding or language production has often been seen as means of exploring theoretical questions in both linguistics and psycholinguistics; the general argument being that, if you can build a computational model of some phenomenon, then you have likely moved some way towards an understanding of that phenomenon. On the other hand, the scope for practical applications of NLP technologies has always been enticing: the idea that we could build truly useful computational artifacts that work with human language goes right back to the origins of the field in the early machine translation experiments of the 1950s. However, it was in the early 1990s that commercial applications of NLP really started to flourish, pushed forward in particular by targeted research in both the USA, much of it funded by the Defense Advanced Research Projects Agency (DARPA) via programs like the Message Understanding Conferences (MUC), and Europe, via a number of large-scale forward-looking EU-funded research programs. -
Intellibot: a Domain-Specific Chatbot for the Insurance Industry
IntelliBot: A Domain-specific Chatbot for the Insurance Industry MOHAMMAD NURUZZAMAN A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy UNSW Canberra at Australia Defence Force Academy (ADFA) School of Business 20 October 2020 ORIGINALITY STATEMENT ‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institute, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project’s design and conception or in style, presentation and linguistic expression is acknowledged.’ Signed Date To my beloved parents Acknowledgement Writing a thesis is a great process to review not only my academic work but also the journey I took as a PhD student. I have spent four lovely years at UNSW Canberra in the Australian Defence Force Academy (ADFA). Throughout my journey in graduate school, I have been fortunate to come across so many brilliant researchers and genuine friends. It is the people who I met shaped who I am today. This thesis would not have been possible without them. My gratitude goes out to all of them. -
Unified Language Model Pre-Training for Natural
Unified Language Model Pre-training for Natural Language Understanding and Generation Li Dong∗ Nan Yang∗ Wenhui Wang∗ Furu Wei∗† Xiaodong Liu Yu Wang Jianfeng Gao Ming Zhou Hsiao-Wuen Hon Microsoft Research {lidong1,nanya,wenwan,fuwei}@microsoft.com {xiaodl,yuwan,jfgao,mingzhou,hon}@microsoft.com Abstract This paper presents a new UNIfied pre-trained Language Model (UNILM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirec- tional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UNILM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UNILM achieves new state-of- the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at https://github.com/microsoft/unilm. 1 Introduction Language model (LM) pre-training has substantially advanced the state of the art across a variety of natural language processing tasks [8, 29, 19, 31, 9, 1]. -
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. -
Grammar Checker for Hindi and Other Indian Languages
International Journal of Scientific & Engineering Research Volume 11, Issue 6, June-2020 1783 ISSN 2229-5518 Grammar Checker for Hindi and Other Indian Languages Anjani Kumar Ray, Vijay Kumar Kaul Center for Information and Language Engineering Mahatma Gandhi Antarrashtriya Hindi Vishwavidyalaya, Wardha (India) Abstract: Grammar checking is one of the sentence is grammatically well-formed. In most widely used techniques within absence of the potential syntactic parsing natural language processing (NLP) approach, incorrect or not-so-well applications. Grammar checkers check the grammatically formed sentences are grammatical structure of sentences based analyzed or produced. The preset paper is on morphological and syntactic an exploratory attempt to devise the hybrid processing. These two steps are important models to identify the grammatical parts of any natural language processing relations and connections of the words to systems. Morphological processing is the phrases to sentences to the extent of step where both lexical words (parts-of- discourse. Language Industry demands speech) and non-word tokens (punctuation such economic programmes doing justice marks, made-up words, acronyms, etc.) are and meeting the expectations of language analyzed into IJSERtheir components. In engineering. syntactic processing, linear sequences of words are transformed into structures that Keywords: Grammar Checking, Language show grammatical relationships among the Engineering, Syntax Processing, POS words in the sentence (Rich and Knight Tagging, Chunking, morphological 1991) and between two or more sentences Analysis joined together to make a compound or complex sentence. There are three main Introduction: Grammar Checker is an approaches/models which are widely used NLP application that helps the user to for grammar checking in a language; write correct sentence in the concerned language. -
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. -
Spell Checking in Computer-Assisted Language Learning: a Study of Misspellings by Nonnative Writers of German
SPELL CHECKING IN COMPUTER-ASSISTED LANGUAGE LEARNING: A STUDY OF MISSPELLINGS BY NONNATIVE WRITERS OF GERMAN Anne Rimrott Diplom, Justus Liebig Universitat, 2002 THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS In the Department of Linguistics O Anne Rimrott 2005 SIMON FRASER UNIVERSITY Spring 2005 All rights reserved. This work may not be reproduced in whole or in part, by photocopy or other means, without permission of the author. APPROVAL Name: Anne Rimrott Degree: Master of Arts Title of Thesis: Spell Checking in Computer-Assisted Language Learning: A Study of Misspellings by Nonnative Writers of German Examining Committee: Chair: Dr. Alexei Kochetov Assistant Professor, Department of Linguistics Dr. Trude Heift Senior Supervisor Associate Professor, Department of Linguistics Dr. Chung-hye Han Supervisor Assistant Professor, Department of Linguistics Dr. Maria Teresa Taboada Supervisor Assistant Professor, Department of Linguistics Dr. Mathias Schulze External Examiner Assistant Professor, Department of Germanic and Slavic Studies University of Waterloo Date DefendedIApproved: SIMON FRASER UNIVERSITY PARTIAL COPYRIGHT LICENCE The author, whose copyright is declared on the title page of this work, has granted to Simon Fraser University the right to lend this thesis, project or extended essay to users of the Simon Fraser University Library, and to make partial or single copies only for such users or in response to a request from the library of any other university, or other educational institution, on its own behalf or for one of its users. The author has further granted permission to Simon Fraser University to keep or make a digital copy for use in its circulating collection. -
Exploiting Wikipedia Semantics for Computing Word Associations
Exploiting Wikipedia Semantics for Computing Word Associations by Shahida Jabeen A thesis submitted to the Victoria University of Wellington in fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science. Victoria University of Wellington 2014 Abstract Semantic association computation is the process of automatically quan- tifying the strength of a semantic connection between two textual units based on various lexical and semantic relations such as hyponymy (car and vehicle) and functional associations (bank and manager). Humans have can infer implicit relationships between two textual units based on their knowledge about the world and their ability to reason about that knowledge. Automatically imitating this behavior is limited by restricted knowledge and poor ability to infer hidden relations. Various factors affect the performance of automated approaches to com- puting semantic association strength. One critical factor is the selection of a suitable knowledge source for extracting knowledge about the im- plicit semantic relations. In the past few years, semantic association com- putation approaches have started to exploit web-originated resources as substitutes for conventional lexical semantic resources such as thesauri, machine readable dictionaries and lexical databases. These conventional knowledge sources suffer from limitations such as coverage issues, high construction and maintenance costs and limited availability. To overcome these issues one solution is to use the wisdom of crowds in the form of collaboratively constructed knowledge sources. An excellent example of such knowledge sources is Wikipedia which stores detailed information not only about the concepts themselves but also about various aspects of the relations among concepts. The overall goal of this thesis is to demonstrate that using Wikipedia for computing word association strength yields better estimates of hu- mans’ associations than the approaches based on other structured and un- structured knowledge sources. -
Chatbot in English Classrooms Encourage Negotiations of Meaning
Chatbot in English Classrooms Encourage Negotiations of Meaning Bachelor’s Thesis of Kelvin Louis, 8th Semester [email protected] Nicola Cocquio, 8th Semester [email protected] University of Applied Sciences and Arts Northwestern Switzerland (FHNW) School of Engineering Computer Science Supervisors Manfred Vogel [email protected] Ivo Nussbaumer [email protected] Brugg-Windisch, 19.03.2019 Abstract Chatbots have become more prevalent in recent years, due to increasing demand as well as improvements in the fields of natural language processing (NLP) and machine learning. Within the field of education research, past studies have rightfully questioned the usefulness of chatbots as means of acquiring a foreign language. A review of the relevant literature shows that the applied chatbots were rule-based and limited to chitchatting in an open-domain. In this thesis we propose an alternate approach to using chatbots in English as a second language (ESL) classrooms. We evaluated the current state of technology to develop a machine learning-based chatbot capable of detecting errors in the students’ language. The chatbot’s domain is confined to interacting with the students in a room reservation roleplay exercise. Prerecorded transcripts of ESL student interactions were used to derive wordings of intents and utterances which served to train and test the chatbot’s machine learning models. Since misspellings are the most common errors in ESL students’ language, a language error detection was introduced into the chatbot’s architecture, providing additional feedback to the students and thereby mitigating repetitive errors. To test the performance of our solution, usability tests and a survey were conducted. -
Words in a Text
Cross-lingual geo-parsing for non-structured data Judith Gelernter Wei Zhang Language Technologies Institute Language Technologies Institute School of Computer Science School of Computer Science Carnegie Mellon University Carnegie Mellon University [email protected] [email protected] ABSTRACT to cross-language geo-information retrieval. We will examine A geo-parser automatically identifies location words in a text. We Strötgen’s view that normalized location information is language- have generated a geo-parser specifically to find locations in independent [16]. Difficulties in cross-language experiments are unstructured Spanish text. Our novel geo-parser architecture exacerbated when the languages use different alphabets, and when combines the results of four parsers: a lexico-semantic Named the focus is on proper names, as in this case, the names of Location Parser, a rules-based building parser, a rules-based street locations. parser, and a trained Named Entity Parser. Each parser has Geo-parsing structured versus unstructured text requires different different strengths: the Named Location Parser is strong in recall, language processing tools. Twitter messages are challenging to and the Named Entity Parser is strong in precision, and building geo-parse because of their non-grammaticality. To handle non- and street parser finds buildings and streets that the others are not grammatical forms, we use a Twitter tokenizer rather than a word designed to do. To test our Spanish geo-parser performance, we tokenizer. We use an English part of speech tagger created for compared the output of Spanish text through our Spanish geo- tweets, which was not available to us in Spanish. -
Basic Version of Multilingual Semantic Text Analysis
V4Design Visual and textual content re-purposing FOR(4) architecture, Design and virtual reality games H2020-779962 D3.3 Basic version of multilingual semantic text analysis Dissemination level: Public Contractual date of delivery: Month 18, 30 June 2019 Actual date of delivery: Month 18, 30 June 2019 Workpackage: WP3 Visual and Textual content analysis Task: T3.2 Entity identification and linking, word sense disambiguation and lexical modelling T3.3 Dependency-based semantic parsing T3.4 Conceptual relation extraction Type: Report Approval Status: Approved Version: 1.2 Number of pages: 64 Filename: D3.3_V4Design_BasicAnalysisTechniques_v1.2.pdf Abstract In this deliverable, we report the advances on the Language Analysis components achieved during the first half of the V4Design project. The components include in particular a multilingual candidate concept detection tool, multilingual dependency parsers, semantic analysers, lexical resources, and a projection of the extracted dependency-based linguistic representations into ontological ones. The information in this document reflects only the author’s views and the European Community is not liable for any use that may be made of the information contained therein. The information in this document is provided as is and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability. Page 1 co-funded by the European Union Page 2 D3.3 – V1.2 History Version Date Reason Revised by 0.1 05/04/2019 Creation