A cognitive linguistic approach to analysis and correction of orthographic errors

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A cognitive linguistic approach to analysis and correction of orthographic errors

Robert Reynolds

Laura Janda

Tore Nesset

Abstract

In this paper, we apply usage-based linguistic analysis to systematize the inventory of orthographic errors observed in the writing of non-native users of Russian. The data comes from a longitudinal corpus (560K tokens) of non-native academic writing. Traditional spellcheckers mark errors and suggest corrections, but do not attempt to model why errors are made. Our approach makes it possible to recognize not only the errors themselves, but also the conceptual causes of these errors, which lie in misunderstandings of Russian phonotactics and morphophonology and the way they are represented by orthographic conventions. With this linguistically-based system in place, we can propose targeted grammar explanations that improve users' command of Russian morphophonology rather than merely correcting errors. Based on errors attested in the non-native academic writing corpus, we introduce a taxonomy of errors, organized by pedagogical domains. Then, on the basis of this taxonomy, we create a set of mal-rules to expand an existing finite-state analyzer of Russian. The resulting morphological analyzer tags wordforms that fit our taxonomy with specific error tags. For each error tag, we also develop an accompanying grammar explanation to help users understand why and how to correct the diagnosed errors. Using our augmented analyzer, we build a webapp to allow users to type or paste a text and receive detailed feedback and correction on common Russian morphophonological and orthographic errors.

Keywords: morphophonology, phonotactics, orthography, corpus, error taxonomy, webapp

Аннотация

Лингвокогнитивный подход к классификации и исправлению орфографических ошибок

Роберт РЕЙНОЛЬДС

Лора ЯНДА

Торе НЕССЕТ

В представленной статье мы предлагаем систематизацию орфографических ошибок неносителей русского языка на основе лингвистических и когнитивных критериев. Материалом исследования послужили данные лонгитюдного корпуса (560000 слов) работ на русском языке, написанных студентами-иностранцами. Традиционные автоматические средства проверки орфографии (spell checkers) выявляют ошибки и предлагают исправления, но не могут построить объяснительные когнитивные модели. Предлагаемый подход позволяет распознать не только сами ошибки, но и концептуальные причины этих ошибок, заключающиеся в непонимании фонотактики и морфофонологии русского языка, а также в способах их репрезентации орфографическими правилами. Этот способ позволяет обосновывать причины грамматических ошибок и рекомендовать правила, которые улучшают владение пользователями русской морфофонологией, а не просто исправляют ошибки. Принцип систематизации аннотированных ошибок в корпусе академического письма на неродном языке и таксономия ошибок ориентированы на преподавание. На основе представленной таксономии мы разработали набор правил (mal-rules), расширяющих функционал конечно-автоматного анализатора русского языка. Разработанный морфологический анализатор аннотирует словоформы специальными тегами ошибок. Для каждого тега ошибки мы предлагаем сопровождающее пояснение, чтобы помочь пользователям понять, почему и как исправить диагностированные ошибки. Используя наш расширенный анализатор, мы создаем веб-приложение, позволяющее пользователям набирать или вставлять текст, а также подробные комментарии и исправления распространенных морфофонологических и орфографических ошибок в русском языке.

Ключевые слова: морфофонология, фонотактика, орфография, корпус, таксономия ошибок

Introduction

cognitive linguistic correction orthographic errors

Traditional approaches to spell checking are sometimes inadequate for the needs of non-native users because they are optimized for native speakers. Not only is it assumed that the user is capable of choosing between suggested corrections, but the suggestions themselves are optimized for the kinds of errors that native speakers make. Even if a non-native user were able to select the correct form from the suggested corrections, it is entirely possible that the user would not understand why it is the correct form in contrast to the form they wrote. Furthermore, whereas spell checking for native speakers is mainly a matter of fixing one-off random errors, non-native users need to acquire rules that they can apply in the future. The mistakes that non-native writers make tend to be systematic, and thereby can be analyzed linguistically and present excellent targeted learning opportunities.

The output of a spellchecker will frequently be either too broad (merely marking a word as misspelled) or too specific (suggesting an alternative for a single given misspelled word) to support the acquisition of useful generalizations. Our proposed tool, the Russian Mentor for Orthographic Rules (RuMOR) is designed to help non-native users connect each specific error to linguistic generalizations, orthographic rules, and examples. This design encourages the user to update their understanding of Russian linguistic and orthographic patterns so that they can avoid making similar errors in the future.

Section 2 reviews related research in the fields of morphological analysis, spelling correction, and intelligent tutoring systems. In Section 3, we describe our methodology, including the process of classifying errors in the RULEC (ENA, April 16, 2022) corpus, modeling the errors in a finite-state framework using mal-rules, evaluating the model, and applying the model in a webapp for users. Section 4 contains a summary of our results and future research directions.

Related work

Our project is connected to research in a number of disparate fields, including Natural Language Processing (NLP), Intelligent Computer-Assisted Language Learning (ICALL), Russian Linguistics, and Second Language Acquisition (SLA).

Pedagogical foundations

Textbooks of Russian typically state spelling rules and contain explanations about pronunciation. However, the connection between this material and what it means for confident writing skills is underrepresented. In other words, students may learn that they should pronounce the letter e like an и when unstressed, or that the letter и sounds like ы when preceded by ш or ж. But students are not warned that these conventions will present challenges in spelling. Furthermore, these rules are typically not exercised in any systematic way and tend to remain peripheral from the students' perspective.

Traditional textbooks take an instruction-based perspective, with the idea of mere transfer of knowledge. A better model for pedagogy is learning by doing, whereby each student constructs their own knowledge network through active engagement. This framework, which is known as constructivism (Biggs 1999, Biggs & Tang 2011), promotes student-centered learning activities both within and outside the classroom. When a student of Russian makes a spelling error, RuMOR can capitalize on that event as an opportunity to engage students with targeted feedback on the relevant spelling and pronunciation conventions. A spelling error is something that is directly relevant to the student in the moment, thus opening up a “teachable moment”, when the student is receptive to improvement of their skills. When used over time, RuMOR will engage each student with all of the typical errors that they need to focus on.

Morphological analysis

The Russian language has widespread fusional morphology, with each major word class having multiple inflection classes. Since the complexity of the morphological system is itself the source of many errors, a morphological analysis is frequently essential for determining what feedback will be most helpful to the user. Table 1 shows two authentic orthographic errors which, at the surface level, appear to be the same -- mistakenly replacing и with е -- but which are motivated by entirely different parts of the linguistic system.

Table 1. Different underlying motivations for identical surface substitutions