DOCSLIB.ORG

Computer Support for the Analysis and Improvement of the Readability of IT-Related Texts

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Computer Support for the Analysis and Improvement of the Readability of IT-Related Texts Department of Informatics TECHNISCHE UNIVERSITÄT MÜNCHEN Master’s Thesis in Information Systems Computer Support for the Analysis and Improvement of the Readability of IT-related Texts Matthias Holdorf Department of Informatics TECHNISCHE UNIVERSITÄT MÜNCHEN Master’s Thesis in Information Systems Computer Support for the Analysis and Improvement of the Readability of IT-related Texts Computergestützte Analyse und Verbesserung der Lesbarkeit von IT-bezogenen Texten Author: Matthias Holdorf Supervisor: Prof. Dr. Florian Matthes Advisor: Bernhard Waltl, M.Sc. Submission Date: 15.11.2016 I confirm that this master’s thesis is my own work and I have documented all sources and materials used. Ich versichere, dass ich diese Master’s Thesis selbstständig verfasst und nur die angegebenen Quellen und Hilfsmittel verwendet habe. München, 15.11.2016 Matthias Holdorf Acknowledgments First and foremost, I would like to thank my advisor Bernhard Waltl and my industry advisor Andreas Zitzelsberger for their preeminent support, interest, and time. I feel fortunate to have had the opportunity to learn from both the academic field and the industry. Furthermore, I would like to thank Prof. Dr. Florian Matthes for his time and feedback, and for providing me the opportunity to write this thesis at the Software Engineering for Business Information Systems (SEBIS) chair, which he holds. I also want to thank my conversation partners Tobias Waltl, Mark Becker, and Henning Femmer. I would like to thank the numerous participants of the quantitative survey, and especially my interview partners. During the search for interview partners, we had an astonishing 100% confirmation rate. Even the managing directors took the time to answer our questions. Such support and appreciation for our work felt overwhelming. I am grateful for the possibility to write my thesis at QAware, which provided us with the environment and technical infrastructure to make this thesis a great project on which I will gladly look back in the future. Abstract Context: A major task in information technology (IT) is communication. Difficult-to-read text hinders the communication between stakeholders and can have expensive consequences. Objectives: We aim to design a tool that decreases the amount of time and resources needed to improve the readability of an IT-related text. Method: We transfer the concept of bug pattern in static code analysis to the readability of text as readability anomalies. The term readability anomaly refers to an indicator of difficult-to-read text passages that may negatively affect communication. To identify the business needs of a software company with a staff of 100 employees, we conducted qualitative interviews and a quantitative survey. Furthermore, we reviewed existing approaches and methodologies from the knowledge base. Subsequently, we designed and implemented a readability checker based on the elicited requirements. Results: The results of the interviews confirmed the assumptions of previous work: Difficult-to-read text hinders communication. The anomaly detection yielded an average precision of 69% with high variation. We investigated the relevance of the true-positive findings with a controlled experiment. Our participants considered 64% of the findings as relevant and would incorporate 59% immediately. Moreover, they were not aware of 48% of the findings. During the application of the tool, the practitioners have incorporated 49% of the overall findings. An analysis of our readability checker takes an average of 40 seconds for 10,000 words. Conclusion: Our readability analysis tool (RAT) can uncover many practically relevant anomalies. Although some readability anomalies need to be adjusted or have to be supported by richer linguistic features, the checker provides effective means to improve the readability of IT-related texts. Based on our application in a practical environment, we found the following requirements and prospects for future work: Improvement of the precision and relevance of anomalies, domain-specific anomalies, configurability of anomaly detection, paraphrasing of detected anomalies, performance of an analysis, integration in the workflow of a company, support of various file formats, and the extent of integration in text processing programs. Keywords: Natural Language Processing, Readability Assessment, Style Checker, Readability Checker, UIMA, DKPro Core, Office Open XML Content 1. Introduction ......................................................................................................................................... 1 1.1 Problem Statement ..................................................................................................................... 2 1.2 Research Approach .................................................................................................................... 3 1.2.1 Behavioral Science ................................................................................................................. 3 1.2.2 Design Science ....................................................................................................................... 4 1.2.3 Research Process ................................................................................................................... 4 1.2.4 Summary ................................................................................................................................. 4 1.3 Contributions .............................................................................................................................. 5 1.3.1 Positioning of Research ........................................................................................................ 5 1.3.2 Research Questions ............................................................................................................... 6 1.4 Outline ......................................................................................................................................... 8 2. Knowledge Base .................................................................................................................................. 9 2.1 Terminology ................................................................................................................................ 9 2.2 Taxonomy of Related Work ................................................................................................... 12 2.2.1 Readability Formulas ........................................................................................................... 12 2.2.2 Spell Checker ........................................................................................................................ 13 2.2.3 Grammar Checker ............................................................................................................... 13 2.2.4 Style and Readability Checker ............................................................................................ 13 2.2.5 Controlled Language Checker ........................................................................................... 14 2.2.6 Text Simplification .............................................................................................................. 15 2.2.7 Paraphrasing ......................................................................................................................... 16 2.3 Academic Approaches ............................................................................................................. 17 2.3.1 MULTILINT ....................................................................................................................... 17 2.3.2 TextLint................................................................................................................................. 18 2.3.3 Smella .................................................................................................................................... 19 2.3.4 DeLite .................................................................................................................................... 20 2.3.5 Coh-Metrix ........................................................................................................................... 22 2.3.6 EasyEnglish .......................................................................................................................... 23 2.4 Industry Approaches ............................................................................................................... 24 2.4.1 LanguageTool ....................................................................................................................... 24 2.4.2 LinguLab ............................................................................................................................... 25 2.4.3 Grammarly ............................................................................................................................ 26 2.5 Overview of Related Work ..................................................................................................... 27 2.6 Discussion ................................................................................................................................. 28 3. Environment ...................................................................................................................................... 29 3.1 Interview Design ...................................................................................................................... 29 3.2 Interview
Load more

Recommended publications
  • Measuring Text Simplification with the Crowd
    Measuring Text Simplification with the Crowd Walter S. Lasecki Luz Rello Jeffrey P. Bigham ROC HCI HCI Institute HCI and LT Institutes Dept. of Computer Science School of Computer Science School of Computer Science University of Rochester Carnegie Mellon University Carnegie Mellon University [email protected] [email protected] [email protected] ABSTRACT the U.S. [41]1 and the European Guidelines for the Production of Text can often be complex and difficult to read, especially for peo­ Easy-to-Read Information in Europe [23]. ple with cognitive impairments or low literacy skills. Text simplifi­ Text simplification is an area of Natural Language Processing cation is a process that reduces the complexity of both wording and (NLP) that attempts to solve this problem automatically by reduc­ structure in a sentence, while retaining its meaning. However, this ing the complexity of both wording (lexicon) and structure (syn­ is currently a challenging task for machines, and thus, providing tax) in a sentence [46]. Previous efforts on automatic text simpli­ effective on-demand text simplification to those who need it re­ fication have focused on people with autism [20, 39], people with mains an unsolved problem. Even evaluating the simplicity of text Down syndrome [48], people with dyslexia [42, 43], and people remains a challenging problem for both computers, which cannot with aphasia [11, 18]. understand the meaning of text, and humans, who often struggle to However, automatic text simplification is in an early stage and agree on what constitutes a good simplification. still is not useful for the target users [20, 43, 48].
    [Show full text]
  • Proceedings of the 1St Workshop on Tools and Resources to Empower
    LREC 2020 Workshop Language Resources and Evaluation Conference 11–16 May 2020 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI) PROCEEDINGS Edited by Nuria´ Gala and Rodrigo Wilkens Proceedings of the LREC 2020 first workshop on Tools and Resources to Empower People with REAding DIfficulties (READI) Edited by: Núria Gala and Rodrigo Wilkens ISBN: 979-10-95546-44-3 EAN: 9791095546443 For more information: European Language Resources Association (ELRA) 9 rue des Cordelières 75013, Paris France http://www.elra.info Email: [email protected] c European Language Resources Association (ELRA) These workshop proceedings are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License ii Preface Recent studies show that the number of children and adults facing difficulties in reading and understanding written texts is steadily growing. Reading challenges can show up early on and may include reading accuracy, speed, or comprehension to the extent that the impairment interferes with academic achievement or activities of daily life. Various technologies (text customization, text simplification, text to speech devices, screening for readers through games and web applications, to name a few) have been developed to help poor readers to get better access to information as well as to support reading development. Among those technologies, text simplification is a powerful way to leverage document accessibility by using NLP techniques. The “First Workshop on Tools and Resources to Empower People with REAding DIfficulties” (READI), collocated with the “International Conference on Language Resources and Evaluation” (LREC 2020), aims at presenting current state-of-the-art techniques and achievements for text simplification together with existing reading aids and resources for lifelong learning, addressing a variety of domains and languages, including natural language processing, linguistics, psycholinguistics, psychophysics of vision and education.
    [Show full text]
  • Data-Driven Text Simplification
    Data-Driven Text Simplification Sanja Štajner and Horacio Saggion [email protected] http://web.informatik.uni-mannheim.de/sstajner/index.html University of Mannheim, Germany [email protected] #TextSimplification2018 https://www.upf.edu/web/horacio-saggion University Pompeu Fabra, Spain COLING 2018 Tutorial Presenters • Sanja Stajner • Horacio Saggion • http://web.informatik.uni- • http://www.dtic.upf.edu/~hsaggion mannheim.de/sstajner • https://www.linkedin.com/pub/horacio- • https://www.linkedin.com/in/sanja- saggion/16/9b9/174 stajner-a6904738 • https://twitter.com/h_saggion • Data and Web Science Group • Large Scale Text Understanding • https://dws.informatik.uni - Systems Lab / TALN group mannheim.de/en/home/ • http://www.taln.upf.edu • University of Mannheim, Germany • Department of Information & Communication Technologies • Universitat Pompeu Fabra, Barcelona, Spain © 2018 by S. Štajner & H. Saggion 2 Tutorial antecedents • Previous tutorials on the topic given at: • IJCNLP 2013 and RANLP 2015 (H. Saggion) • RANLP 2017 (S. Štajner) • Automatic Text Simplification. H. Saggion. 2017. Morgan & Claypool. Synthesis Lectures on Human Language Technologies Series. https://www.morganclaypool.com/doi/abs/10.2200/S00700ED1V01Y201602 HLT032 © 2018 by S. Štajner & H. Saggion 3 Outline • Motivation for ATS • Automatic text simplification • TS projects • TS resources • Neural text simplification © 2018 by S. Štajner & H. Saggion 4 PART 1 Motivation for Text Simplification © 2018 by S. Štajner & H. Saggion 5 Text Simplification (TS) The process of transforming a text into an equivalent which is more readable and/or understandable by a target audience • During simplification, complex sentences are split into simple ones and uncommon vocabulary is replaced by more common expressions • TS is a complex task which encompasses a number of operations applied at different linguistic levels: • Lexical • Syntactic • Discourse • Started to attract the attention of natural language processing some years ago (1996) mainly as a pre-processing step © 2018 by S.
    [Show full text]
  • Classifier-Based Text Simplification for Improved Machine Translation
    Classifier-Based Text Simplification for Improved Machine Translation Shruti Tyagi Deepti Chopra Iti Mathur Nisheeth Joshi Department of Computer Department of Computer Department of Computer Department of Computer Science Science Science Science Banasthali University Banasthali University Banasthali University Banasthali University Rajasthan, India Rajasthan, India Rajasthan, India Rajasthan, India [email protected] [email protected] [email protected] [email protected] Abstract— Machine Translation is one of the research fields E. Text Summarization: Splitting of sentence or conversion of of Computational Linguistics. The objective of many MT long sentence into smaller ones helps in text summarization. Researchers is to develop an MT System that produce good quality and high accuracy output translations and which also There are also different approaches through which we can covers maximum language pairs. As internet and Globalization is apply text simplification. These are: increasing day by day, we need a way that improves the quality of translation. For this reason, we have developed a Classifier based Text Simplification Model for English-Hindi Machine A. Lexical Simplification: In this approach, identification of Translation Systems. We have used support vector machines and complex words takes place and generation of Naïve Bayes Classifier to develop this model. We have also evaluated the performance of these classifiers. substitutes/synonyms takes place accordingly. For example, in the example below, we have highlighted the complex words Keywords— Machine Translation, Text Simplification, Naïve and their respective substitutes. Bayes Classifier, Support Vector Machine Classifier Original Sentence: Audible word was originally named audire in Latin. I. INTRODUCTION Simplified Sentence: Audible word was first called audire in Text Simplification is the process that reduces the Latin.
    [Show full text]
  • Natural Language Processing Tools for Reading Level Assessment and Text Simplification for Bilingual Education
    Natural Language Processing Tools for Reading Level Assessment and Text Simplification for Bilingual Education Sarah E. Petersen A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2007 Program Authorized to Offer Degree: Computer Science & Engineering University of Washington Graduate School This is to certify that I have examined this copy of a doctoral dissertation by Sarah E. Petersen and have found that it is complete and satisfactory in all respects, and that any and all revisions required by the final examining committee have been made. Chair of the Supervisory Committee: Mari Ostendorf Reading Committee: Mari Ostendorf Oren Etzioni Henry Kautz Date: In presenting this dissertation in partial fulfillment of the requirements for the doctoral degree at the University of Washington, I agree that the Library shall make its copies freely available for inspection. I further agree that extensive copying of this dissertation is allowable only for scholarly purposes, consistent with “fair use” as prescribed in the U.S. Copyright Law. Requests for copying or reproduction of this dissertation may be referred to Proquest Information and Learning, 300 North Zeeb Road, Ann Arbor, MI 48106-1346, 1-800-521-0600, to whom the author has granted “the right to reproduce and sell (a) copies of the manuscript in microform and/or (b) printed copies of the manuscript made from microform.” Signature Date University of Washington Abstract Natural Language Processing Tools for Reading Level Assessment and Text Simplification for Bilingual Education Sarah E. Petersen Chair of the Supervisory Committee: Professor Mari Ostendorf Electrical Engineering Reading proficiency is a fundamental component of language competency.
    [Show full text]
  • Experimenting Sentence Split-And-Rephrase Using Part-Of-Speech Labels
    Experimenting Sentence Split-and-Rephrase Using Part-of-Speech Labels P. Berlanga Neto and E. Y. Okano and E. E. S. Ruiz Departamento de Computação e Matemática, FFCLRP Universidade de São Paulo (USP). Av. Bandeirantes, 3900, Monte Alegre. 14040-901, Ribeirão Preto, SP – Brazil [pauloberlanga, okano700, evandro]@usp.br Abstract. Text simplification (TS) is a natural language transformation process that reduces linguistic complexity while preserving semantics and retaining its original meaning. This work aims to present a research proposal for automatic simplification of texts, precisely a split-and-rephrase approach based on an encoder-decoder neural network model. The proposed method was trained against the WikiSplit English corpus with the help of a part-of-speech tagger and obtained a BLEU score validation of 74.72%. We also experimented with this trained model to split-and-rephrase sentences written in Portuguese with relative success, showing the method’s potential. CCS Concepts: • Computing methodologies → Natural language processing. Keywords: natural language processing, neural networks, sentence simplification 1. INTRODUCTION Text Simplification (TS) is the process of modifying natural language to reduce complexity and im- prove both readability and understandability [Shardlow 2014]. A simplified vocabulary or a simplified text structure can benefit different publics, such as people with low education levels, children, non- native individuals, people who have learning disorders (such as autism, dyslexia, and aphasia), among others [Štajner et al. 2015]. Although the simplicity of a text seems intuitively obvious, it does not have a precise definition in technical terms. Traditional assessment metrics consider factors such as sentence length, syllable count, and other linguistic features of the text to identify it as elaborate or not [Shardlow 2014].
    [Show full text]
  • Arxiv:2005.00352V2 [Cs.CL] 16 Apr 2021
    MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases Louis Martin1;2 Angela Fan1;3 Éric de la Clergerie2 Antoine Bordes1 Benoît Sagot2 1Facebook AI Research, Paris, France 2Inria, Paris, France 3LORIA, Nancy, France {louismartin, angelafan, abordes}@fb.com {eric.de_la_clergerie, benoit.sagot}@inria.fr Abstract in multiple ways, and depend on the target audi- Progress in sentence simplification has been ence. Simplification guidelines are not uniquely hindered by a lack of labeled parallel simpli- defined, outlined by the stark differences in English fication data, particularly in languages other simplification benchmarks (Alva-Manchego et al., than English. We introduce MUSS, a Multi- 2020a). This highlights the need for more general lingual Unsupervised Sentence Simplification models that can adjust to different simplification system that does not require labeled simplifica- contexts and scenarios. tion data. MUSS uses a novel approach to sen- In this paper, we propose to train controllable tence simplification that trains strong models using sentence-level paraphrase data instead models using sentence-level paraphrase data only, of proper simplification data. These models i.e. parallel sentences that have the same meaning leverage unsupervised pretraining and control- but phrased differently. In order to generate sim- lable generation mechanisms to flexibly adjust plifications and not paraphrases, we use ACCESS attributes such as length and lexical complex- (Martin et al., 2020) to control attributes such as ity at inference time. We further present a length, lexical and syntactic complexity. Para- method to mine such paraphrase data in any phrase data is more readily available, and opens the language from Common Crawl using semantic sentence embeddings, thus removing the need door to training flexible models that can adjust to for labeled data.
    [Show full text]
  • Text Simplification Using Typed Dependencies
    Text Simplification using Typed Dependencies: A Comparison of the Robustness of Different Generation Strategies Advaith Siddharthan University of Aberdeen Department of Computing Science [email protected] Abstract The original police inquiry, which led to Mulcaire being jailed in 2007, also dis- We present a framework for text simplification covered evidence that he has successfully based on applying transformation rules to a intercepted voicemail messages belonging typed dependency representation produced by to Rebekah Brooks, who was editor of the the Stanford parser. We test two approaches Sun when Mulcaire was working exclu- to regeneration from typed dependencies: (a) sively for its Sunday stablemate. gen-light, where the transformed dependency graphs are linearised using the word order and to: morphology of the original sentence, with any changes coded into the transformation rules, The original police inquiry led to Mulcaire and (b) gen-heavy, where the Stanford depen- being jailed in 2007. The police inquiry dencies are reduced to a DSyntS representa- also discovered evidence that he has suc- tion and sentences are generating formally us- ing the RealPro surface realiser. The main cessfully intercepted voicemail messages contribution of this paper is to compare the belonging to Rebekah Brooks. Rebekah robustness of these approaches in the presence Brooks was editor of the Sun. This was of parsing errors, using both a single parse and when Mulcaire was working exclusively an n-best parse setting in an overgenerate and for its Sunday stablemate. rank approach. We find that the gen-light ap- proach is robust to parser error, particularly in The main aim of this paper is to describe and com- the n-best parse setting.
    [Show full text]
  • Workshopabstracts
    TENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION MAY 23 – 28, 2016 GRAND HOTEL BERNARDIN CONFERENCE CENTRE Portorož , SLOVENIA WORKSHOP ABSTRACTS Editors: Please refer to each single workshop list of editors. Assistant Editors: Sara Goggi, Hélène Mazo The LREC 2016 Proceedings are licensed under a Creative Commons Attribution- NonCommercial 4.0 International License LREC 2016, TENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION Title: LREC 2016 Workshop Abstracts Distributed by: ELRA – European Language Resources Association 9, rue des Cordelières 75013 Paris France Tel.: +33 1 43 13 33 33 Fax: +33 1 43 13 33 30 www.elra.info and www.elda.org Email: [email protected] and [email protected] ISBN 978-2-9517408-9-1 EAN 9782951740891 ii TABLE OF CONTENTS W12 Joint 2nd Workshop on Language & Ontology (LangOnto2) & Terminology & Knowledge Structures (TermiKS) …………………………………………………………………………….. 1 W5 Cross -Platform Text Mining & Natural Language Processing Interoperability ……. 12 W39 9th Workshop on Building & Using Comparable Corpora (BUCC 2016)…….…….. 23 W19 Collaboration & Computing for Under-Resourced Languages (CCURL 2016).….. 31 W31 Improving Social Inclusion using NLP: Tools & Resources ……………………………….. 43 W9 Resources & ProcessIng of linguistic & extra-linguistic Data from people with various forms of cognitive / psychiatric impairments (RaPID)………………………………….. 49 W25 Emotion & Sentiment Analysis (ESA 2016)…………..………………………………………….. 58 W28 2nd Workshop on Visualization as added value in the development, use & evaluation of LRs (VisLR II)……….……………………………………………………………………………… 68 W15 Text Analytics for Cybersecurity & Online Safety (TA-COS)……………………………… 75 W13 5th Workshop on Linked Data in Linguistics (LDL-2016)………………………………….. 83 W10 Lexicographic Resources for Human Language Technology (GLOBALEX 2016)… 93 W18 Translation Evaluation: From Fragmented Tools & Data Sets to an Integrated Ecosystem?..................................................................................................................
    [Show full text]
  • Par4sim – Adaptive Paraphrasing for Text Simplification
    Par4Sim – Adaptive Paraphrasing for Text Simplification Seid Muhie Yimam Chris Biemann Language Technology Group Department of Informatics, MIN Faculty Universität Hamburg, Germany {yimam,biemann}@informatik.uni-hamburg.de Abstract Learning from a real-world data stream and continuously updating the model without explicit supervision is a new challenge for NLP applications with machine learning components. In this work, we have developed an adaptive learning system for text simplification, which improves the underlying learning-to-rank model from usage data, i.e. how users have employed the system for the task of simplification. Our experimental result shows that, over a period of time, the performance of the embedded paraphrase ranking model increases steadily improving from a score of 62.88% up to 75.70% based on the NDCG@10 evaluation metrics. To our knowledge, this is the first study where an NLP component is adaptively improved through usage. Title and Abstract in Amharic Par4Sim -- የሚጣጣም መልሶ መጻፍ፤ ፅሁፍን ለማቅለል ከገሃዱ ዓለም የመረጃ ፍሰት መማርና ቀጥተኛ ድጋፍ ሳያስፈልገው ሞዴሉን በተከታታይ ለማሻሻል ማሽን ለርኒንግ (በራሱ መማር የሚችል የኮምፒዩተር መርሃ ግብር) በሚያካትቱ የተፈጥሯዊ የቋንቋ ቴክኖሎጂ (ተ.ቋ.ቴ - NLP) ጥናት አዲስ ፈተና ሆኗል። በዚህ ምርምር፤ ተጠቃሚዎች እንድን መተግበሪያ በመጠቀም ላይ ሳሉ ማለትም በተጠቃሚዎች የተለያዩ ድርጊቶች መሰረት ውህቦችን ከበስተጀርባ ሆኖ በመሰብስሰብ የማሽን ለርኒንጉን ሞዴል በማዘመን እየተጣጣመ መማር የሚችል የጽሁፍ አቅላይ ስርዓት ሰርተናል። የሲስተማችንን ውጤታማነት ለማረጋገጥ፤ አንድን ፅሁፍ ለተለያዩ ተጠቃሚዎች በቀላሉ እንዲገባቸው ለማድረግ ተጣጣሚ ትርጓሜዎችን እንደ ምሳሌ ወስደናል። በመሆኑም፤ አንድ ተጠቃሚ አንድን ፅሁፍ መረዳት ሲያቅተው፤ የኛን ተንታኝ መተግበሪያ ተጠቅሞ አማራጭ ቃል ወይንም ሐረግ በመተካት ፅሁፍን የበለጠ ቀላል ማድረግ ይችላል። የሙከራ ውጤታችን እንደሚያሳየው ከሆነ፤ በተከታታይ ጊዜ ውስጥ እንዲማር/እንዲላመድ በሲስተሙ ውስጥ የተሸጎጠው ደረጃ መዳቢ ሞዴል፤ በNDCG@10 የግምገማ ልኬቶች መሰረት፣ ሳያቋርጥ በመጨመር ከ62.88% እስከ 75.70% ድረስ መሻሻል አሳይቷል። እኛ እስከምናውቀው ድረስ፤ ይህ ጥናት፤ የተፈጥሯዊ የቋንቋ ቴክኖሎጂ (ተ.ቋ.ቴ - NLP) ትግበራዎች በአግልግሎት አጠቃቀም ጊዜ ውህቦችን በመሰብስብ የማሽን ለርኒንግ ስልተ ቀመር ሞዴል አገልግሎት በሂደት ውስጥ እያለ እንዲሻሻል/እንዲላመድ የሚያደርግ የመጀመሪያው ጥናት ነው። 1 Introduction Traditionally, training data for machine learning-based applications are collected amass, using a specific annotation tool.
    [Show full text]
  • FREE CAT TOOLS AS an ALTERNATIVE to COMMERCIAL SOFTWARE: Omegat
    FACULTAD DE TRADUCCIÓN E INTERPRETACIÓN Grado en Traducción e Interpretación TRABAJO FIN DE GRADO FREE CAT TOOLS AS AN ALTERNATIVE TO COMMERCIAL SOFTWARE: OmegaT Presentado por Veronica Nicoleta Anica Tutelado por Ana María Alconchel Soria, 2014 Free CAT tools as an alternative to commercial software: OmegaT Content ACKNOWLEDGEMENT ............................................................................................................... 4 I. INTRODUCTION ....................................................................................................................... 6 1. Connection with competencies ............................................................................................ 7 1.1. General competencies ................................................................................................... 7 1.2. Specific competencies ................................................................................................... 8 1. PURPOSE ............................................................................................................................. 10 2. METHODOLOGY ................................................................................................................... 11 II. THEORETICAL APPROACH ................................................................................................... 13 1. Translation and technology ................................................................................................ 13 1.1. Technological advances and the process of globalization
    [Show full text]
  • Online Spanish for Kids
    Learn more online • www.lingualinkup.com ONLINE SPANISH FOR KIDS TOOL FOR PROOFREADING IN SPANISH – LANGUAGETOOL REVIEW I used to struggle with Spanish grammar when creating content, but this tool I’m reviewing today is great for proofreading your Spanish online (spell checker & grammar checker in Spanish). The name is LanguageTool and it has helped me learn about the accents (á, é, í, ó, ú, ü, ñ, ¿, ¡) on certain words, or if I got a word spelled wrong, the el/la wrong, and in many more corrections. LanguageTool for Spanish Proofreading: https://languagetool.org/ It corrects more than just misspellings, it also corrects words in sentences that should be different. If you haven’t checked it out you should, test it out for a few days because it will truly help you to correct your Spanish. You can use it in all of your emails, Google Docs, Google Chrome, Firefox, Microsoft Word, LibreOffice, and it can also be used offline – no need for the internet to use it. Screenshots of Some of our Blogs Before Editing Here are a few examples where we used the tool in a Google Doc. This first example is where you can see how much the tool can correct mistakes in Spanish: LEARN SPANISH | ONLINE SPANISH FOR KIDS | 1 Learn more online • www.lingualinkup.com ONLINE SPANISH FOR KIDS However, as we see in these two next examples, the tool isn’t perfect. And so keep that in mind that it will make the wrong suggestions at times if the mistake is too difficult for LanguageTool to figure it out.
    [Show full text]