Writing system

Summary

A writing system is a method of visually representing verbal communication, based on a script and a set of rules regulating its use. While both writing and speech are useful in conveying messages, writing differs in also being a reliable form of information storage and transfer. Writing systems require shared understanding between writers and readers of the meaning behind the sets of characters that make up a script. Without a mutual understanding of the meanings behind both writing and reading (via things like literacy, reading comprehension, transliteration, and translation), a writing system can be rendered useless. Writing is usually recorded onto a durable medium, such as paper or electronic storage, although non-durable methods may also be used, such as writing on a computer display, on a blackboard, in sand, or by skywriting. Reading a text can be accomplished purely in the mind as an internal process, or expressed orally. Writing systems can be placed into broad categories such as alphabets, syllabaries, or logographies, although any particular system may have attributes of more than one category. In the alphabetic category, a standard set of letters represent speech sounds. In a syllabary, each symbol correlates to a syllable or mora. In a logography, each character represents a semantic unit such as a word or morpheme. Abjads differ from alphabets in that vowels are not indicated, and in abugidas or alphasyllabaries each character represents a consonant–vowel pairing. Alphabets typically use a set of less than 100 symbols to fully express a language, whereas syllabaries can have several hundred, and logographies can have thousands of symbols. Many writing systems also include a special set of symbols known as punctuation which is used to aid interpretation and help capture nuances and variations in the message's meaning that are communicated verbally by cues in timing, tone, accent, inflection or intonation. Writing systems were preceded by proto-writing, which used pictograms, ideograms and other mnemonic symbols.

Official source

https://en.wikipedia.org/wiki/Writing_system

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This paper proposes new ways to assess handwriting, a critical skill in any child’s school journey. Traditionally, a pen and paper test called the BHK test (Concise Evaluation Scale for Children’s Handwriting) is used to assess children’s handwriting in French-speaking countries. Any child with a BHK score above a certain threshold is diagnosed as ‘dysgraphic’, meaning that they are then eligible for financial coverage for therapeutic support. We previously developed a version of the BHK for tablet computers which provides rich data on the dynamics of writing (acceleration, pressure, and so forth). The underlying model was trained on dysgraphic and non-dysgraphic children. In this contribution, we deviate from the original BHK for three reasons. First, in this instance, we are interested not in a binary output but rather a scale of handwriting difficulties, from the lightest cases to the most severe. Therefore, we wish to compute how far a child’s score is from the average score of children of the same age and gender. Second, our model analyses dynamic features that are not accessible on paper; hence, the BHK is useful in this instance. Using the PCA (Principal Component Analysis) reduced the set of 53 handwriting features to three dimensions that are independent of the BHK. Nonetheless, we double-checked that, when clustering our data set along any of these three axes, we accurately detected dysgraphic children. Third, dysgraphia is an umbrella concept that embraces a broad variety of handwriting difficulties. Two children with the same global score can have totally different types of handwriting difficulties. For instance, one child could apply uneven pen pressure while another one could have trouble controlling their writing speed. Our new test not only provides a global score, but it also includes four specific score for kinematics, pressure, pen tilt and static features (letter shape). Replacing a global score with a more detailed profile enables the selection of remediation games that are very specific to each profile.

2020

Character-level Neural Machine Translation(NMT) models have recently achieved impressive results on many language pairs. They mainly do well for Indo-European language pairs, where the languages share the same writing system. However, for translating between Chinese and English, the gap between the two different writing systems poses a ma-jor challenge because of a lack of systematic correspondence between the individual linguistic units.In this paper, we enable character-level NMT for Chinese, by breaking down Chinese characters into linguistic units similar to that of Indo-European languages. We use the Wubi encoding scheme, which preserves the original shape and semantic in-formation of the characters, while also being reversible. We show promising results from training Wubi-based models on the character-and subword-level with recurrent as well as convolutional models.

Association for Computational Linguistics2018

Quantifying Collaboration in Synchronous Document Editing

Adrian Pierre Sergio Pace, Louis Antoine Baligand, Jennifer Kaitlyn Olsen, Stian Haklev

Collaborative synchronous writing tools like Google Docs and Etherpad let multiple users edit the same document and see each others edits in near real-time to simplify collaboration and avoid merge-conflicts. These tools are used extensively across many domains, including education, in both research and industry. The very nature of needing to constantly synchronize state between multiple users means that very granular editing data is automatically captured and stored. In theory, this data could provide important insights into the editing process, the contributions of the different users, how the text developed over time, and other questions relevant to researchers studying writing from different theoretical and methodological angles. However, this extreme granularity of the data (down to individual key presses), makes analysis very complex. Most of the research focused on automatic analysis of collaborative writing to date has focused on asynchronous writing, and looked at the "diffs" between one editing session and the next. In this paper, we present a method and a tool to construct informative operations from text data, as well as preliminary metrics for measuring the collaborative writing process. Additionally, our method adds to previous work in that it can be used to assess the writing during the writing process rather than just being applied to an end product.

2018