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Ni ar lurade gott folk


Skip to main content. Log In Sign Up. In addition, other writing errors above word-level are Ni ar lurade gott folk here, including punc- tuation 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 gram- mars 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.

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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. This fact motivates the need for ad- aptation 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 "Ni ar lurade gott folk" 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.

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.

My main Ni ar lurade gott folk Robin Cooper awakened the idea of using finite state meth- ods for grammar checking and launched the collaboration with the Xerox research group. I want to express my greatest gratitude to him for inspiring discussions dur- ing 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 Ni ar lurade gott folk ences.

I also want to thank all my friends, who reminded me now and Ni ar lurade gott folk about life outside the university. My deepest gratitude to my family for being there for me and for always believ- "Ni ar lurade gott folk" 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 A Grammar Error Detector 6.

A great deal of our com- munication 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 email are among the most usual activities on computers. Other communicated media that enable written communication are also becoming popular such as webchat or in- stant messaging on the Internet or text messaging Short-Message-Service, SMS via the mobile phone.

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 pictures2 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 phys- ically shaping letters with a pen shifts toward the more cognitive processes of text 1 Studies of computer-mediated communication are provided by e.

Severinson Eklundh ; Crystal ; Herring Writing on a computer enhances in general both the motivation to write, revise or completely change a text cf. Wresch, ; Daiute, ; Severinson Eklundh, ; Ponte- corvo, The status of written language in our modern information society has de- veloped. In contrast to ancient times, writing is no longer reserved for just a small minority of professional groups e.

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. 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. Gram- mar checkers that recognize syntactic errors and often also violations of punc- tuation, word capitalization conventions, number and date formatting and other style-related issues, thus working above the word level, are a rather new techno- logy, especially for such minor small languages like Swedish.

In addition to the present work, there are three research groups working in this area. Development of this tool has occurred over a series of projects starting in Domeij et al. Finally, a Finnish language engineering company Lingsoft Inc.

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Initiated inand completed inthis tool was released on the market in Novemberand has been part of the Swedish Microsoft Office Package since Arppe, ; Birn, 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 Ni ar lurade gott folk 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 prob- ably 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. The role of proofreading tools for educational purposes is a rather new application area and this work can be "Ni ar lurade gott folk" a Ni ar lurade gott folk step in that direction.

Here is a brief characterization of these three tasks: 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 questions that arise are: What grammar errors occur? How should the errors be Ni ar lurade gott folk 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? 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. What is the nature of the error? What is the diagnosis of the error? What is needed to be able to detect the error?

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How are the grammar errors handled by the current Swedish grammar checkers, Grammatifix, Granska and Scarrie? Implementation of the detection of some grammar errors: A subset of errors will be chosen for implementation Ni ar lurade gott folk will concern grammar check- ing to the level of detecting errors.

Errors will obtain a description of the type of error detected. Implementation will not include any additional dia- gnosis or any suggestion of how to correct the error. The analysis will be shallow, using finite state techniques.

The grammars will describe real syn- tactic 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. 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 com- puters 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.

The second part of the thesis concerns grammar checking and includes three chapters. Chapter 5 starts with a Ni ar lurade gott folk overview of the requirements and functionalities of a grammar checker and what is required for the er- rors in the present data. Swedish grammar checkers are described and their performance is checked on the present data.

Chapter 6 presents the imple- mentation of a grammar error detector that handles these errors, including description of finite state formalism. The techniques of finite state parsing are explained.

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Chapter 7 presents the performance of this tool. The thesis ends with a concluding summary Chapter 8. In addition, the thesis contains four appendices. Ap- pendix B presents the error corpora consisting of the grammar errors found in the present study Appendix B. The tagset used is presented in Appendix C and some listings from the implementation are listed in Appendix D. Rather, learning to write is a process of learning the difference between written language and the already acquired spoken language.

Consequently, errors that one will find in the writing of primary school children often are due to their lack of knowledge of written language and consist of attempts to reproduce spoken language norms as an alternative to the standard written norm or to errors due to the as yet not acquired part of written language.

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The method worn in the implemented gadget FiniteCheck grips subtraction of finite majestic automata that represent grammars with varying degrees of detail, creating a car that classifies phrases in a workbook containing trustworthy kinds of errors. The current idea of the system handles errors as to agreement in noun phrases, and verb selection of finite and non-finite forms.

At the lexical knock down, we seize all lexical tags to words and do not use a tagger which could upper crust information in incorrect main body text that authority be needed later to find the error. At higher levels, structural indistinctness is treated by parsing order, grammar extension and some other heuristics.

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Identifying incorrect words and phrases is the most obvious task of a grammar checker. Chapter 5 starts with a general overview of the requirements and functionalities of a grammar checker and what is required for the er- rors in the present data. Then my brother came with a rubber-boat and lifted me up. Kongen har etter bokens utgivelse ikke vist noen statsmannsfakter! Although this measure does not reveal anything about what units are actually delimited, there seems to be a tendency for mean sentence length to increase with age.

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