My New Teaching Partner? Using the Grammar Checker in Writing Instruction

Total Page:16

File Type:pdf, Size:1020Kb

My New Teaching Partner? Using the Grammar Checker in Writing Instruction Reva Potter and Dorothy Fuller Seventh-grade English My New Teaching teacher Reva Potter and education professor Partner? Using the Dorothy Fuller collaborate on a study that shows that Grammar Checker in using computer grammar checkers may improve Writing Instruction students’ confi dence and their understanding of key grammatical concepts. omeone else is teaching my (Reva’s) shoulder assistance of the teacher. The word processor students grammar terminology, is one of the most common applications used, yet usage, and mechanics. Actually, language arts teachers may not address the issues S something else is teaching my stu- associated with grammar-check software. They may dents grammar, and although I do not always agree also overlook the capability of this omnipresent tool with this teacher, I have decided to embrace it. It is to teach grammar in a relevant and engaging way. tireless, relentless, and follows my students from Research on word processing grammar check- one composition to another on their computers. My ers is limited but provides insights about the capa- new teaching partner is the word processor’s gram- bilities and concerns related to the use of grammar mar checker. checkers in the classroom. Research suggests three The grammar checker does not intimidate me main ideas: teachers should approach grammar or make me fear for my job as an English teacher; in checkers critically; students have limited compe- fact, it makes me realize that in tency with the grammar tools; and classroom The word processor is today’s electronic writing envi- instruction can incorporate use of the grammar one of the most common ronments, the students need me checker (Jensen; McGee and Ericsson; Vernon). applications used, yet more than ever. For instance, First and foremost, teachers must look at language arts teachers my students need someone to grammar-check programs thoughtfully. As Tim may not address the explain why the powerful gram- McGee and Patricia Ericsson so aptly put it, “Mind- mar checker does not correct lessly accepting a piece of software is irresponsible— issues associated with such sentences as “Little Women even if everyone in the world is using it, even if we grammar-check software. were a great book,” or “The can’t really change it, even if we’re afraid of breaking cows or the pig fi nd the grass.” it” (465). Before teachers ask students to use the I have discovered that my students can learn from the software critically, they must be critical themselves. grammar checker, but not without my guidance. Teachers should carefully consider the use of the grammar checker due to its sophisticated and yet sometimes fl awed recommendations to writers. For Grammar at Our Fingertips instance, in an examination of the performance of Even for those who are not language arts teachers, WordPerfect and Word grammar checkers with 36 grammar instruction is diffi cult to avoid. As uni- common grammatical errors, Alex Vernon found versal as a blinking cursor, it is waiting on our lap- that the WordPerfect checker correctly identifi ed 17 top, home, school, and workplace computers. Young of the errors and the Word checker found 12 (340). and old alike, cautiously or carelessly, we are all Teachers need to be aware of the limited feedback tutored by the word processor’s grammar checker. the grammar checker provides to their students. The grammar checker slipped quietly into our Grammar checkers do not claim to teach classrooms, allowing students to make improve- grammar; they are tools to bring potential prob- ments to their documents without the over-the- lems to the writer’s attention. They also offer only 36 English Journal 98.1(2008): 36–41 Reva Potter and Dorothy Fuller formal and Standard English preferences, limiting Working with Students to Make Choices the freer expression of some literary forms. Without about Grammar Curriculum guidance, students may misuse the checker, become I introduced my seventh graders to the grammar frustrated, and feel discouraged. Users must be per- checker by having them type their essays with gram- ceptive about accepting and rejecting the recom- mar tools fi rst deactivated. mendations, and students of writing who currently When we reactivated the Teachers should carefully use the grammar checker may not have the critical grammar checker and the consider the use of the knowledge to do this (McGee and Ericsson 461). wavy green lines appeared, Possibilities exist for the use of grammar grammar checker due to students clicked one error at checkers in the writing classroom. Vernon recom- its sophisticated and yet a time to gather the names mends teaching the checker’s limitations and how sometimes fl awed for all of the errors the students might work with these (336), including checker disclosed. Students recommendations activities where learners respond to grammar-check recorded the names of the to writers. recommendations in small groups, make corrections errors in their documents, on highlighted errors without the help of computer exactly as the errors were described. “Comma Use,” suggestions, create sentences to trigger the gram- “Fragment,” and “Passive Sentences” were some mar checker or fool it, and compare rules in the common errors found by the students. grammar checker to rules in the grammar handbook Back in the classroom, students worked in (346). Jensen suggests that by using the grammar small groups to compare their lists of errors and checker’s readability statistics, students could also tally all the types. All class tallies were combined revise their written work, varying their sentence and recorded on the board, and after all three class types and structures, to manipulate the grammar- periods had reported, the fi nal tally was ready for check readability score of their documents (28). the following day’s discussion. The list included 50 different types of errors. Beginning Teacher Research The next day in each class period, we dis- with the Grammar Checker cussed each error type listed. Each class chose the My experiences with the advantages and frustra- errors it found most interesting and wanted to tions of the grammar checker caused me to wonder know what every error meant. If I had had a full if my students would benefi t by using the tool more day to explain them all, I think the students would consciously. Initially, I brought grammar-check have stayed and listened. I pointed out that these activities into my classroom to stimulate my stu- grammar-check terms were the same as those in dents’ curiosity for the tool. I also hoped their new their language arts textbooks; in fact, most were knowledge would allow them to navigate the gram- part of the curriculum we would study. mar checker independently and connect grammar So which error types did they want to study rules and terms to electronic composition. fi rst? Many students suggested we study passive To gather evidence of the potential of the voice because they see it all the time when typing at grammar checker as a learning tool, my mentor a computer but did not know what it meant. I told (and co-author) Dorothy and I designed an action them that choice was interesting because passive research project on the use of the grammar checker voice fi rst appears in our standards in seventh grade, in my classroom. Three main research questions so it would be a great unit to choose. They also pro- guided this exploration: (1) When given direct posed to study comma use because it appeared so instruction with the word-processing grammar often in their documents. I explained that we could checker, will students improve as critical, confi dent focus our comma study on compound and complex users of this tool? (2) When combining grammar sentence structure, one common area of diffi culty for instruction with grammar-check tools, will stu- both student and adult writers. For the fi nal unit I dents improve their understanding of key grammar persuaded students to study subject-verb agreement concepts? (3) Is the seventh-grade language arts based on how diffi cult these errors can be for the curriculum a highly appropriate place for instruc- grammar checker to detect. It is also one of those tion with the grammar checker? diffi cult areas for both adolescents and adults. English Journal 37 My New Teaching Partner? Using the Grammar Checker in Writing Instruction Connecting the Grammar and hypothesized why the computer grammar Checker to Instruction checker may have missed or misdiagnosed an error. I realized this was a favorite activity when a student We designed the four-month action research study said, “That was cool. When do we do it again?” In to include direct instruction of the grammar subsequent units students eagerly typed their checker and regular grammar instruction enhanced assigned “pretest” sentences, typed extra if they had with use of grammar-check tools. Students fi rst time, and began hypothesizing at their individual learned about the checker, its components and pur- computers about the accuracy of the grammar poses, before beginning the agreed-on three gram- checker before the results were reported. mar topics. Once into the Another engaging use of the grammar check At the start of the units, lessons incorporated allowed students to personalize their grammar grammar check in a number study, students using experience by creating original sentence examples of ways. Students composed the computer for to challenge the checker: practicing examples of or typed essays with the gram- composition were likely active or passive voice, creating possible subject- mar-check tools turned off verb agreement problems, and changing simple to ignore a grammar- and on; they wrote sentences sentences to compound or complex. Students check explanation. to “trigger” grammar-check watched the computer screens as the checker error identifi cation; they com- “reacted” to the sentences they created, and they pared terminology and rules of grammar from text compared and discussed the checker’s recommenda- resources with those on the computer checker; and tions with their classmates.
Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • Using a Grammar Checker for Detection Translated English Sentence
    Using a Grammar Checker for Detection Translated English Sentence Nay Yee Lin, Khin Mar Soe, Ni Lar Thein University of Computer Studies, Yangon [email protected], [email protected] Abstract verification of the structure of a sentence and of relations between words (e.g. agreement). Machine Translation Systems expect target Grammar checkers are most often language output to be grammatically correct implemented as a feature of a larger program, within the frame of proper grammatical such as a word processor. Although all major category. In Myanmar-English statistical Open Source word processors offer spell machine translation system, the proposed system checking and grammar checker feature. Such a concerns with the target language model to feature is not available as a separate free program smooth the translated English sentences. Most of either for machine translation. Therefore, our the typical grammar checkers can detect approach is a free program which can be used ungrammatical sentences and seek for what both as a stand-alone grammar checker. error it is. However, they often fail to detect Three methods are widely used for grammar grammar errors for translated English sentence checking in a language; syntax-based checking, such as missing words. Therefore, we intend to statistics-based checking and rule-based develop an ongoing grammar checker for checking. In syntax based grammar checking , English as a second language (ESL). There are each sentence is completely parsed to check the two main tasks in this approach: detecting the grammatical correctness of it. The text is sentence pattern in chunk structure and considered incorrect if the syntactic parsing fails.
    [Show full text]
  • Various Techniques Used for Grammar Checking
    www.ijcait.com International Journal of Computer Applications & Information Technology Vol. 9, Issue I (Special Issue on NLP) 2016 (ISSN: 2278-7720) Various Techniques Used For Grammar Checking Blossom Manchanda Vijay Anant Athavale Sanjeev kumar Sharma Assistant Professor Director Assistant professor Department of CSE Gulzar Group of Institutions DAV University GNDEC Ludhiana Khanna Jalandhar ABSTRACT Grammar checker is one of proofing tool used for syntactic analysis of the text. Various techniques are used for development of grammar checker. These techniques includes rule based technique, statistical based technique and syntax based technique. In this research article, all these three techniques have been discussed. Both advantages and disadvantages of these techniques have also been discussed at the end. Keywords Grammar checker, rule based, statistical technique, syntax based technique 1. INTRODUCTION Grammar checker of a language is a system that detects various grammatical errors in a given text based on the grammar of that particular language, and reports those errors to the user along with a list of helpful suggestions to rectify those errors. It is also expected to provide the error details including the error reason to explain that why a particular error is being dubbed a grammatical error. A grammar checker not only detects grammatical errors, but also suggests possible corrections. Grammar checkers are also part of the fundamental tools line word processor needed for NLP in any language. Till date best supported language in NLP is English. A lot of work has gone into developing sophisticated systems that have gone into widespread use, such as automatic translators, grammar checker and spell checkers.
    [Show full text]
  • Spell Checker in CET Designer
    Linköping University | Department of Computer Science Bachelor thesis, 16 ECTS | Datateknik 2016 | LIU-IDA/LITH-EX-G--16/069--SE Spell checker in CET Designer Rasmus Hedin Supervisor : Amir Aminifar Examiner : Zebo Peng Linköpings universitet SE–581 83 Linköping +46 13 28 10 00 , www.liu.se Upphovsrätt Detta dokument hålls tillgängligt på Internet – eller dess framtida ersättare – under 25 år från publiceringsdatum under förutsättning att inga extraordinära omständigheter uppstår. Tillgång till dokumentet innebär tillstånd för var och en att läsa, ladda ner, skriva ut enstaka kopior för enskilt bruk och att använda det oförändrat för ickekommersiell forskning och för undervisning. Överföring av upphovsrätten vid en senare tidpunkt kan inte upphäva detta tillstånd. All annan användning av dokumentet kräver upphovsmannens medgivande. För att garantera äktheten, säkerheten och tillgängligheten finns lösningar av teknisk och admin- istrativ art. Upphovsmannens ideella rätt innefattar rätt att bli nämnd som upphovsman i den omfattning som god sed kräver vid användning av dokumentet på ovan beskrivna sätt samt skydd mot att dokumentet ändras eller presenteras i sådan form eller i sådant sam- manhang som är kränkande för upphovsmannenslitterära eller konstnärliga anseende eller egenart. För ytterligare information om Linköping University Electronic Press se förlagets hemsida http://www.ep.liu.se/. Copyright The publishers will keep this document online on the Internet – or its possible replacement – for a period of 25 years starting from the date of publication barring exceptional circum- stances. The online availability of the document implies permanent permission for anyone to read, to download, or to print out single copies for his/hers own use and to use it unchanged for non-commercial research and educational purpose.
    [Show full text]
  • Many Shades of Grammar Checking – Launching a Constraint Grammar
    Many shades of grammar checking – Launching a Constraint Grammar tool for North Sámi Linda Wiechetek [email protected] Sjur Moshagen [email protected] Børre Gaup [email protected] Thomas Omma [email protected] Divvun UiT Norgga árktalaš universitehta 1 Introduction Version Date Divvun 1.0 (2007-12-12) This paper discusses the characteristics and evalu- Divvun 1.0.1 (2007-12-21) ation of the very first North Sámi spell- and gram- Divvun 1.1 (2008-12-17) mar checker. At its launch it supports MS Word Divvun 2.0 (2010-12-08) and GoogleDocs1, cf. Figure 1. We describe Divvun 2.1 (2011-03-21) its component parts, the technology used, such Divvun 2.2 (2011-11-23) as Constraint Grammar (Karlsson, 1990; Karlsson Divvun 2.3 (2013-02-08) et al., 1995; Bick and Didriksen, 2015) and Hfst- Divvun 2.3 (2013-02-08) pmatch (Hardwick et al., 2015), and its evaluation Divvun 3.0 (2013-06-13) with a new evaluation tool specifically designed Divvun 4.0 (2015-12-17) for that purpose. Divvun 4.0.1 (2016-03-17) Only the modules of the full-scale grammar Divvun 4.1 (2016-12-15) checker described in Wiechetek (2017) that have Divvun 4.2 (2018-12-20) been tested and improved sufficiently for the pub- GramDivvun 1.0 beta (2019-09-27) lic to use are released in this launch. More advanced syntactic errors will be included at Table 1: Divvun release history (2007-2019) a later stage. The grammar checker modules are an enhancement to an existing North Sámi spellchecker (Gaup et al., 2006), following a phi- tools developed by the Divvun group.
    [Show full text]
  • Detecting and Correcting Syntactic Errors in Machine Translation Using Feature-Based Lexicalized Tree Adjoining Grammars
    Computational Linguistics and Chinese Language Processing Vol. 17, No. 4, December 2012, pp. 1-14 1 © The Association for Computational Linguistics and Chinese Language Processing Detecting and Correcting Syntactic Errors in Machine Translation Using Feature-Based Lexicalized Tree Adjoining Grammars Wei-Yun Ma∗, and Kathleen McKeown∗ Abstract Statistical machine translation has made tremendous progress over the past ten years. The output of even the best systems, however, is often ungrammatical because of the lack of sufficient linguistic knowledge. Even when systems incorporate syntax in the translation process, syntactic errors still result. To address this issue, we present a novel approach for detecting and correcting ungrammatical translations. In order to simultaneously detect multiple errors and their corresponding words in a formal framework, we use feature-based lexicalized tree adjoining grammars, where each lexical item is associated with a syntactic elementary tree, in which each node is associated with a set of feature-value pairs to define the lexical item’s syntactic usage. Our syntactic error detection works by checking the feature values of all lexical items within a sentence using a unification framework. In order to simultaneously detect multiple error types and track their corresponding words, we propose a new unification method which allows the unification procedure to continue when unification fails and also to propagate the failure information to relevant words. Once error types and their corresponding words are detected, one is able to correct errors based on a unified consideration of all related words under the same error types. In this paper, we present some simple mechanism to handle part of the detected situations.
    [Show full text]
  • Selected Grand Challenges in Cognitive Science
    Selected Grand Challenges in Cognitive Science Search Engine for the Real World Test Taker Report Generator Reading to Learn Prepared by the MITRE Corporation for DARPA IPTO October 2005 Approved for Public Release; Distribution Unlimited. Case Number 05-1218. ©2005-The MITRE Corporation. All rights reserved. Overview This compilation of Selected Grand Challenges in Cognitive Science includes challenge specifications, briefings, supplemental materials, and FY’06 plans for 4 challenges selected by DARPA IPTO: Search Engine for the Real World, Test Taker, Report Generator, and Reading to Learn. The overview summarizes each challenge and highlights future plans. The rest of the document is divided into individual challenge sections. Each section consists of a summary page and brief descriptions of the attached materials. Search Engine for the Real World: Grand Treasure Hunt Adventure Mobile robots use navigation and visual recognition to discover objects in a real world treasure hunt Participants have 20 minutes to locate a treasure within a house. In order to uncover the identity and location of the treasure, the robot participants will be given hints to find 10 other required objects along the way, some of which may reveal additional clues. In FY’06, we plan to conduct a proof-of-concept treasure hunt to assess challenge feasibility and establish baseline capability. We propose to construct a sample house, implement a communication protocol, and collaborate with UMass Lowell and MITRE robot teams to test and refine. Test Taker: Taking the SAT An autonomous system will take the SAT® and score in the 50th percentile of high school students taking the examination.
    [Show full text]
  • Design and Evaluation of Grammar Checkers in Multiple Languages
    Design and evaluation of grammar checkers in multiple languages Antje HELFRICH Bradley MUSIC NLG, Microsoft Corp. NLG, Microsoft Corp. 1 Microsoft Way 1 Microsoft Way Redmond, WA 98052 Redmond, WA 98052 [email protected] [email protected] Abstract This paper describes issues involved in the development of a grammar checker in multiple languages at Microsoft Corporation. Focus is on design (selecting and prioritizing error identification rules) and evaluation (determining product quality). is to create documents as efficiently as possible. Introduction We don’t want to distract, delay or bother people The goal of the project discussed here is to develop with a picky proofing component that points out French, German and Spanish grammar checkers linguistic issues most users don’t care about (even for a broad user base consisting of millions of if we could critique those with high precision1)or Microsoft Word customers – users who create eagerly highlights any “suspicious” sentence with a documents of all types, styles and content, using potential problem. Instead, we focus on critiques various terminology and dialects, and who want that are actually helpful to the majority of users proofing tools that help eliminate mistakes in an from their point of view and support them in their efficient and non-intruding fashion. ultimate goal of creating grammatically clean The fact that the user base is so broad documents efficiently. poses many challenges, among them the questions of which errors are most common among such a Researching the customer diverse set of users, and what types of input the The first step towards determining the feature set is grammar checkers need to be tested and evaluated to describe the target user of the grammar checker.
    [Show full text]