Automatic Detection of Grammar Errors in Primary School Children s Texts A Finite State Approach

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1 GOTHENBURG MONOGRAPHS IN LINGUISTICS 24 Automatic Detection of Grammar Errors in Primary School Children s Texts A Finite State Approach Sylvana Sofkova Hashemi Doctoral Dissertation Publicly defended in Lilla Hörsalen, Humanisten, Göteborg University, on June 7, 2003, at for the degree of Doctor of Philosophy Department of Linguistics, Göteborg University, Sweden

2 ISBN c 2003 Sylvana Sofkova Hashemi Typeset by the author using LATEX Printed by Intellecta Docusys, Göteborg, Sweden, 2003

3 i Abstract This thesis concerns the analysis of grammar errors in Swedish texts written by primary school children and the development of a finite state system for finding such errors. Grammar errors are more frequent for this group of writers than for adults and the distribution of the error types is different in children s texts. In addition, other writing errors above word-level are discussed here, including punctuation and spelling errors resulting in existing words. The method used in the implemented tool FiniteCheck involves subtraction of finite state automata that represent grammars with varying degrees of detail, creating a machine that classifies phrases in a text containing certain kinds of errors. The current version of the system handles errors concerning agreement in noun phrases, and verb selection of finite and non-finite forms. At the lexical level, we attach all lexical tags to words and do not use a tagger which could eliminate information in incorrect text that might be needed later to find the error. At higher levels, structural ambiguity is treated by parsing order, grammar extension and some other heuristics. The simple finite state technique of subtraction has the advantage that the grammars one needs to write to find errors are always positive, describing the valid rules of Swedish rather than grammars describing the structure of errors. The rule sets remain quite small and practically no prediction of errors is necessary. The linguistic performance of the system is promising and shows comparable results for the error types implemented to other Swedish grammar checking tools, when tested on a small adult text not previously analyzed by the system. The performance of the other Swedish tools was also tested on the children s data collected for this study, revealing quite low recall rates. This fact motivates the need for adaptation of grammar checking techniques to children, whose errors are different from those found in adult writers and pose more challenge to current grammar checkers, that are oriented towards texts written by adult writers. The robustness and modularity of FiniteCheck makes it possible to perform both error detection and diagnostics. Moreover, the grammars can in principle be reused for other applications that do not necessarily have anything to do with error detection, such as extracting information in a given text or even parsing. KEY WORDS: grammar errors, spelling errors, punctuation, children s writing, Swedish, language checking, light parsing, finite state technology

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5 iii Acknowledgements Work on this thesis would not have been possible without contributions, support and encouragement from many people. The idea of developing a writing tool for supporting children in their text production and grammar emerged from a study on how primary school children write by hand in comparison to when they use a computer. Special thanks to my colleague Torbjörn Lager, who inspired me to do this study and whose children attended the school where I gathered my data. My main supervisor Robin Cooper awakened the idea of using finite state methods for grammar checking and launched the collaboration with the Xerox research group. I want to express my greatest gratitude to him for inspiring discussions during project meetings and supervision sessions, and his patience with my writing, struggling to understand every bit of it, always raising questions and always full of new exciting ideas. I really enjoyed our discussions and look forward to more. I would also like to thank my assistant supervisor Elisabet Engdahl who carefully read my writing and made sure that I expressed myself more clearly. Many thanks to all my colleagues at the Department of Linguistics for creating an inspiring research environment with interesting projects, seminars and conferences. I especially want to mention Leif Grönqvist for being the helping hand next door whenever, Robert Andersson for being my project colleague, Stina Ericsson for loan of LATEX-manual and for always being helpful, Ulla Veres for help with recruitment of new victims for writing experiments, Jens Allwood and Elisabeth Ahlsén for introducing me to the world of transcription and coding, Sally Boyd, Nataliya Berbyuk, Ulrika Ferm for support and encouragement, Shirley Nicholson for always available with books and also milk for coffee, Pia Cromberger always ready for a chat. A special thanks to Ylva Hård af Segerstad for fruitful discussions leading to future collaboration that I am looking forward to, and for being a friend. I also want to thank the children in my study and their teachers for providing me with their text creations, and Sven Strömqvist and Victoria Johansson for sharing their data collection. A special thanks to Genie Perdin who carefully proofread this thesis and gave me some encouraging last minute kicks. I also want to thank all my friends, who reminded me now and then about life outside the university. My deepest gratitude to my family for being there for me and for always believing in me. My husband Ali - I know the way was long and there were times I could be distant, but I am back. My daughter Sarah for being the sunshine of my life, my inspiration, my everything. My mother, father, sister and my big little brother... Sylvana Sofkova Hashemi Göteborg, May 2003

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7 v Table of Contents 1 Introduction Written Language in a Computer Literate Society Aim and Scope of the Study Outline of the Thesis I Writing 7 2 Writing and Grammar Introduction Research on Writing in General Written Language and Computers Learning to Write The Influence of Computers on Writing Studies of Grammar Errors Introduction Primary and Secondary Level Writers Adult Writers Conclusion Data Collection and Analysis Introduction Data Collection Introduction The Sub-Corpora Error Categories Introduction Spelling Errors Grammar Errors Spelling or Grammar Error? Punctuation Types of Analysis Error Coding and Tools Corpus Formats CHAT-format and CLAN-software

8 vi 4 Error Profile of the Data Introduction General Overview Grammar Errors Agreement in Noun Phrases Agreement in Predicative Complement Definiteness in Single Nouns Pronoun Case Verb Form Sentence Structure Word Choice Reference Other Grammar Errors Distribution of Grammar Errors Summary Child Data vs. Other Data Primary and Secondary Level Writers Evaluation Texts of Proof Reading Tools Scarrie s Error Database Summary Real Word Spelling Errors Introduction Spelling in Swedish Segmentation Errors Misspelled Words Distribution of Real Word Spelling Errors Summary Punctuation Introduction General Overview of Sentence Delimitation The Orthographic Sentence Punctuation Errors Summary Conclusions

9 vii II Grammar Checking Error Detection and Previous Systems Introduction What Is a Grammar Checker? Spelling vs. Grammar Checking Functionality Performance Measures and Their Interpretation Possibilities for Error Detection Introduction The Means for Detection Summary and Conclusion Grammar Checking Systems Introduction Methods and Techniques in Some Previous Systems Current Swedish Systems Overview of The Swedish Systems Summary Performance on Child Data Introduction Evaluation Procedure The Systems Detection Procedures The Systems Detection Results Overall Detection Results Summary and Conclusion FiniteCheck: A Grammar Error Detector Introduction Finite State Methods and Tools Finite State Methods in NLP Regular Grammars and Automata Xerox Finite State Tool Finite State Parsing System Architecture Introduction The System Flow Types of Automata The Lexicon Composition of The Lexicon The Tagset

10 viii Categories and Features Broad Grammar Parsing Parsing Procedure The Heuristics of Parsing Order Further Ambiguity Resolution Parsing Expansion and Adjustment Narrow Grammar Noun Phrase Grammar Verb Grammar Error Detection and Diagnosis Introduction Detection of Errors in Noun Phrases Detection of Errors in the Verbal Head Summary Performance Results Introduction Initial Performance on Child Data Performance Results: Phase I Grammatical Coverage Flagging Accuracy Current Performance on Child Data Introduction Improving Flagging Accuracy Performance Results: Phase II Overview of Performance on Child Data Performance on Other Text Performance Results of FiniteCheck Performance Results of Other Tools Overview of Performance on Other Text Summary and Conclusion Summary and Conclusion Introduction Summary Introduction Children s Writing Errors Diagnosis and Possibilities for Detection Detection of Grammar Errors

11 ix 8.3 Conclusion Future Plans Introduction Improving the System Expanding Detection Generic Tool? Learning to Write in the Information Society Bibliography 260 Appendices 276 A Grammatical Feature Categories 279 B Error Corpora 281 B.1 Grammar Errors B.2 Misspelled Words B.3 Segmentation Errors C SUC Tagset 313 D Implementation 315 D.1 Broad Grammar D.2 Narrow Grammar: Noun Phrases D.3 Narrow Grammar: Verb Phrases D.4 Parser D.5 Filtering D.6 Error Finder

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13 LIST OF TABLES xi List of Tables 3.1 Child Data Overview General Overview of Sub-Corpora General Overview by Age General Overview of Spelling Errors in Sub-Corpora General Overview of Spelling Errors by Age Number Agreement in Swedish Gender Agreement in Swedish Definiteness Agreement in Swedish Noun Phrases with Proper Nouns as Head Noun Phrases with Pronouns as Head Noun Phrases without (Nominal) Head Agreement in Partitive Noun Phrase in Swedish Gender and Number Agreement in Predicative Complement Personal Pronouns in Swedish Finite and Non-finite Verb Forms Tense Structure Fa-sentence Word Order Af-sentence Word Order Distribution of Grammar Errors in Sub-Corpora Distribution of Grammar Errors by Age Examples of Grammar Errors in Teleman s Study Examples of Grammar Errors from the Skrivsyntax Project Grammar Errors in the Evaluation Texts of Grammatifix Grammar Errors in Granska s Evaluation Corpus General Error Ratio in Grammatifix, Granska and Child Data Three Error Types in Grammatifix, Granska and Child Data Grammar Errors in Scarrie s ECD and Child Data Examples of Spelling Error Categories Spelling Variants Distribution of Real Word Segmentation Errors Distribution of Real Word Spelling Errors in Sub-Corpora Distribution of Real Word Spelling Errors by Age Sentence Delimitation in the Sub-Corpora Sentence Delimitation by Age Major Delimiter Errors in Sub-Corpora Major Delimiter Errors by Age Comma Errors in Sub-Corpora

14 xii LIST OF TABLES 4.37 Comma Errors by Age Summary of Detection Possibilities in Child Data Overview of the Grammar Error Types in Grammatifix (GF), Granska (GR) and Scarrie (SC) Overview of the Performance of Grammatifix, Granska and Scarrie Performance Results of Grammatifix on Child Data Performance Results of Granska on Child Data Performance Results of Scarrie on Child Data Performance Results of Targeted Errors Some Expressions and Operators in XFST Types of Directed Replacement Noun Phrase Types Performance Results on Child Data: Phase I False Alarms in Noun Phrases: Phase I False Alarms in Finite Verbs: Phase I False Alarms in Verb Clusters: Phase I False Alarms in Noun Phrases: Phase II False Alarms in Finite Verbs: Phase II False Alarms in Verb Clusters: Phase II Performance Results on Child Data: Phase II Performance Results of FiniteCheck on Other Text Performance Results of Grammatifix on Other Text Performance Results of Granska on Other Text Performance Results of Scarrie on Other Text

15 LIST OF FIGURES xiii List of Figures 3.1 Principles for Error Categorization Grammar Error Distribution Error Density in Sub-Corpora Error Density in Age Groups Three Error Types in Grammatifix (black line), Granska (gray line) and Child Data (white line) Error Distribution of Selected Error Types in Scarrie Error Distribution of Selected Error Types in Child Data The System Architecture of FiniteCheck False Alarms: Phase I vs. Phase II Overview of Recall in Child Data Overview of Precision in Child Data Overview of Overall Performance in Child Data Overview of Recall in Other Text Overview of Precision in Other Text Overview of Overall Performance in Other Text

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17 Chapter 1 Introduction 1.1 Written Language in a Computer Literate Society Written language plays an important role in our society. A great deal of our communication occurs by means of writing, which besides the traditional paper and pen, is facilitated by the computer, the Internet and other applications such as for instance the mobile phone. Word processing and sending messages via are among the most usual activities on computers. Other communicated media that enable written communication are also becoming popular such as webchat or instant messaging on the Internet or text messaging (Short-Message-Service, SMS) via the mobile phone. 1 The present doctoral dissertation concerns word processing on computers, in particular the linguistic tools integrated in such authoring aids. The use of word processors for writing both in educational and professional settings modifies the process, practice and acquisition of writing. With a word processor, it is not only easy to produce a text with a neat layout, but it supports the writer throughout the whole writing process. Text may be restructured and revised at any time during text production without leaving any trace of the changes that have been made. Text may be reused and a new text composed by cutting and pasting passages. Iconic material such as pictures 2 (or even sounds) can be inserted, linguistic aids can be used for proofreading a text. Writing acquisition can be enhanced by use of a word processor. For instance, focus on somewhat more technical aspects such as physically shaping letters with a pen shifts toward the more cognitive processes of text 1 Studies of computer-mediated communication are provided by e.g. Severinson Eklundh (1994); Crystal (2001); Herring (2001). A recent dissertation by Hård af Segerstad (2002) explores especially how written Swedish is used in , webchat and SMS. 2 Smileys or emoticons (e.g. :-) happy face ) are more used in computer-mediated communication.

18 2 Chapter 1. production enabling the writer to apply the whole language register. Writing on a computer enhances in general both the motivation to write, revise or completely change a text (cf. Wresch, 1984; Daiute, 1985; Severinson Eklundh, 1993; Pontecorvo, 1997). The status of written language in our modern information society has developed. In contrast to ancient times, writing is no longer reserved for just a small minority of professional groups (e.g. priests and monks, bankers, important merchants). In particular, the emergence of computers in writing has led to the involvement of new user groups besides today s writing professionals like journalists, novelists and scientists. We write more nowadays in general, and the freedom of and control over one s own writing has increased. Texts are produced rapidly and are more seldom proofread by a careful secretary with knowledge of language. This is sometimes reflected in the quality and correctness of the resulting text (cf. Severinson Eklundh, 1995). Linguistic tools that check mechanics, grammar and style have taken over the secretarial function to some degree and are usually integrated in word processing software. Spelling checkers and hyphenators that check writing mechanics and identify violations on individual words have existed for some time now. Grammar checkers that recognize syntactic errors and often also violations of punctuation, word capitalization conventions, number and date formatting and other style-related issues, thus working above the word level, are a rather new technology, especially for such minor small languages like Swedish. Grammar checking tools for languages such as English, French, Dutch, Spanish, and Greek were being developed in the 1980 s, whereas research on Swedish writing aids aimed at grammatical deviance started quite recently. In addition to the present work, there are three research groups working in this area. The Department of Numerical Analysis and Computer Science (NADA) at the Royal Institute of Technology (KTH), with a long tradition of research in writing and authoring aids, is responsible for Granska. Development of this tool has occurred over a series of projects starting in 1994 (Domeij et al., 1996, 1998; Carlberger et al., 2002). The Department of Linguistics, Uppsala University was involved in an EU-sponsored project, Scarrie, between 1996 and The goal of this project was development of language tools for Danish, Norwegian and Swedish (Sågvall Hein, 1998a; Sågvall Hein et al., 1999). Finally, a Finnish language engineering company Lingsoft Inc. developed Grammatifix. Initiated in 1997, and completed in 1999, this tool was released on the market in November 1998, and has been part of the Swedish Microsoft Office Package since 2000 (Arppe, 2000; Birn, 2000). The three Swedish systems mainly use parsing techniques with some degree of feature relaxation and/or explicit error rules for detection of errors. Grammatifix and Granska are developed as generic tools and are tested on adult (mostly pro-

19 Introduction 3 fessional) texts. Scarrie s end-users are professional writers from newspapers and publishing firms. 1.2 Aim and Scope of the Study The primary purpose of the present work is to detect grammar errors by means of linguistic descriptions of correct language use rather than describing the structure of errors. The ideal would be to develop a generic method for detection of grammar errors in unrestricted text that could be applied to different writing populations displaying different error types without the need for rewriting the grammars of the system. That is, instead of describing the errors made by different groups of writers resulting in distinct sets of error rules, use the same grammar set for detection. This approach of identifying errors in text without explicit description of them contrasts with the other three Swedish grammar checkers. Using this method, we will hopefully cover many different cases of errors and minimize the possibility of overlooking some errors. We chose primary school children as the targeted population as a new group of users not covered by the previous Swedish projects. Children as beginning writers, are in the process of acquiring written language, unlike adult writers, and will probably produce relatively more errors and errors of a different kind than adult writers. Their writing errors have probably more to do with competence than performance. Grammar checkers for this group have to have different coverage and concentrate on different kinds of errors. Further, the positive impact of computers on children s writing opens new opportunities for the application of language technology. The role of proofreading tools for educational purposes is a rather new application area and this work can be considered a first step in that direction. Against this background, the main goal of the present thesis is handling children s errors and experimenting with positive grammatical descriptions using finite state techniques. The work is divided into three subtasks, including first, an overall error analysis of the collected children s texts, then exploring the nature and possibilities for detection of errors and finally, implementation of detection of (some) grammatical error types. Here is a brief characterization of these three tasks: I. Investigation of children s writing errors: The targeted data for a grammar checker can be selected either by intuitions about errors that will probably occur, or by directly looking at errors that actually occur. In the present work, the second approach of empirical analysis will be applied. Texts from pupils at three primary schools were collected and analyzed for errors, focusing on errors above word-level including grammar errors, spelling errors resulting in existent words, and punctuation. The main focus lies on grammar errors

20 4 Chapter 1. as the basis for implementation. The questions that arise are: What grammar errors occur? How should the errors be categorized? What spelling errors result in lexicalized strings and are not captured by a spelling checker? What is the nature of these? How is punctuation used and what errors occur? II. Investigation of the possibilities for detection of these writing errors: The nature of errors will be explored along with available technology that can be applied in order to detect them. An interesting point is how the errors that are found are handled by the current systems. The questions that arise are: What is the nature of the error? What is the diagnosis of the error? What is needed to be able to detect the error? How are the grammar errors handled by the current Swedish grammar checkers, Grammatifix, Granska and Scarrie? III. Implementation of the detection of (some) grammar errors: A subset of errors will be chosen for implementation and will concern grammar checking to the level of detecting errors. Errors will obtain a description of the type of error detected. Implementation will not include any additional diagnosis or any suggestion of how to correct the error. The analysis will be shallow, using finite state techniques. The grammars will describe real syntactic relations rather than the structure of erroneous patterns. The difference between grammars of distinct accuracy will reveal the errors, that as finite state automata can be subtracted from each other. Karttunen et al. (1997a) use this technique to find instances of invalid dates and this is an attempt to apply their approach to a larger language domain. The work on this grammar error detector started at the Department of Linguistics at Göteborg University in 1998, in the project Finite State Grammar for Finding Grammatical Errors in Swedish Text and was a collaboration with the NADA group at KTH in the project Integrated Language Tools for Writing and Document Handling. 3 The present thesis describes both the initial development within this project and the continuation of it. The main contributions of this thesis concern understanding of incorrect language use in primary school children s writing and computational analysis of such incorrect text by means of correct language use, in particular: Collection of texts written by primary school children, written both by hand and on a computer. 3 This project was sponsored by HSFR/NUTEK Language Technology Programme and has its site at:

21 Introduction 5 Analysis of grammar errors, spelling errors and punctuation in the texts of primary school writers. Comparison of errors found in the present data with errors found in other studies on grammar errors. Comparison of error types covered by the three Swedish grammar checkers. Performance analysis of the three Swedish grammar checkers on the present data. Implementation of a grammar error detector that derives/compiles error patterns rather than writing the error grammar by hand. Performance analysis of the detector on the collected data and some portion of other data. 1.3 Outline of the Thesis The remaining chapters of the thesis fall into two parts. Part I: The first part is devoted to a discussion of writing and an analysis of the collected data and consists of three chapters. Chapter 2 provides a brief introduction to research on writing in general, writing acquisition, how computers influence writing and descriptions of previous findings on grammar errors, concluding with what grammar errors are to be expected in written Swedish. Chapter 3 gives an overview of the data collected and a discussion of error classification. Chapter 4 presents the error profile of the data. The chapter concludes with discussion of the requirements for a grammar error detector for the particular subjects of this study. Part II: The second part of the thesis concerns grammar checking and includes three chapters. Chapter 5 starts with a general overview of the requirements and functionalities of a grammar checker and what is required for the errors in the present data. Swedish grammar checkers are described and their performance is checked on the present data. Chapter 6 presents the implementation of a grammar error detector that handles these errors, including description of finite state formalism. The techniques of finite state parsing are explained. Chapter 7 presents the performance of this tool. The thesis ends with a concluding summary (Chapter 8). In addition, the thesis contains four appendices. Appendix A presents the grammatical feature categories

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