Elena Volodina


2022

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Generation of Synthetic Error Data of Verb Order Errors for Swedish
Judit Casademont Moner | Elena Volodina
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)

We report on our work-in-progress to generate a synthetic error dataset for Swedish by replicating errors observed in the authentic error annotated dataset. We analyze a small subset of authentic errors, capture regular patterns based on parts of speech, and design a set of rules to corrupt new data. We explore the approach and identify its capabilities, advantages and limitations as a way to enrich the existing collection of error-annotated data. This work focuses on word order errors, specifically those involving the placement of finite verbs in a sentence.

2021

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CoDeRooMor: A new dataset for non-inflectional morphology studies of Swedish
Elena Volodina | Yousuf Ali Mohammed | Therese Lindström Tiedemann
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

The paper introduces a new resource, CoDeRooMor, for studying the morphology of modern Swedish word formation. The approximately 16.000 lexical items in the resource have been manually segmented into word-formation morphemes, and labeled for their categories, such as prefixes, suffixes, roots, etc. Word-formation mechanisms, such as derivation and compounding have been associated with each item on the list. The article describes the selection of items for manual annotation and the principles of annotation, reports on the reliability of the manual annotation, and presents tools, resources and some first statistics. Given the”gold” nature of the resource, it is possible to use it for empirical studies as well as to develop linguistically-aware algorithms for morpheme segmentation and labeling (cf statistical subword approach). The resource will be made freely available.

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Proceedings of the 10th Workshop on NLP for Computer Assisted Language Learning
David Alfter | Elena Volodina | Ildikó Pilan | Johannes Graën | Lars Borin
Proceedings of the 10th Workshop on NLP for Computer Assisted Language Learning

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DaLAJ – a dataset for linguistic acceptability judgments for Swedish
Elena Volodina | Yousuf Ali Mohammed | Julia Klezl
Proceedings of the 10th Workshop on NLP for Computer Assisted Language Learning

2020

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Proceedings of the 9th Workshop on NLP for Computer Assisted Language Learning
David Alfter | Elena Volodina | Ildikó Pilan | Herbert Lange | Lars Borin
Proceedings of the 9th Workshop on NLP for Computer Assisted Language Learning

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Towards Privacy by Design in Learner Corpora Research: A Case of On-the-fly Pseudonymization of Swedish Learner Essays
Elena Volodina | Yousuf Ali Mohammed | Sandra Derbring | Arild Matsson | Beata Megyesi
Proceedings of the 28th International Conference on Computational Linguistics

This article reports on an ongoing project aiming at automatization of pseudonymization of learner essays. The process includes three steps: identification of personal information in an unstructured text, labeling for a category, and pseudonymization. We experiment with rule-based methods for detection of 15 categories out of the suggested 19 (Megyesi et al., 2018) that we deem important and/or doable with automatic approaches. For the detection and labeling steps,we use resources covering personal names, geographic names, company and university names and others. For the pseudonymization step, we replace the item using another item of the same type from the above-mentioned resources. Evaluation of the detection and labeling steps are made on a set of manually anonymized essays. The results are promising and show that 89% of the personal information can be successfully identified in learner data, and annotated correctly with an inter-annotator agreement of 86% measured as Fleiss kappa and Krippendorff’s alpha.

2019

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LEGATO: A flexible lexicographic annotation tool
David Alfter | Therese Lindström Tiedemann | Elena Volodina
Proceedings of the 22nd Nordic Conference on Computational Linguistics

This article is a report from an ongoing project aiming at analyzing lexical and grammatical competences of Swedish as a Second language (L2). To facilitate lexical analysis, we need access to metalinguistic information about relevant vocabulary that L2 learners can use and understand. The focus of the current article is on the lexical annotation of the vocabulary scope for a range of lexicographical aspects, such as morphological analysis, valency, types of multi-word units, etc. We perform parts of the analysis automatically, and other parts manually. The rationale behind this is that where there is no possibility to add information automatically, manual effort needs to be added. To facilitate the latter, a tool LEGATO has been designed, implemented and currently put to active testing.

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Proceedings of the 8th Workshop on NLP for Computer Assisted Language Learning
David Alfter | Elena Volodina | Lars Borin | Ildikó Pilan | Herbert Lange
Proceedings of the 8th Workshop on NLP for Computer Assisted Language Learning

2018

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Towards Single Word Lexical Complexity Prediction
David Alfter | Elena Volodina
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications

In this paper we present work-in-progress where we investigate the usefulness of previously created word lists to the task of single-word lexical complexity analysis and prediction of the complexity level for learners of Swedish as a second language. The word lists used map each word to a single CEFR level, and the task consists of predicting CEFR levels for unseen words. In contrast to previous work on word-level lexical complexity, we experiment with topics as additional features and show that linking words to topics significantly increases accuracy of classification.

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Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors
Ildikó Pilán | Elena Volodina
Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

The presence of misspellings and other errors or non-standard word forms poses a considerable challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We investigate, therefore, an unsupervised method where correction candidates for Swedish language learners’ errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding error correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors.

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Investigating the importance of linguistic complexity features across different datasets related to language learning
Ildikó Pilán | Elena Volodina
Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing

We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consideration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pinpoint fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions.

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Proceedings of the 7th workshop on NLP for Computer Assisted Language Learning
Ildikó Pilán | Elena Volodina | David Alfter | Lars Borin
Proceedings of the 7th workshop on NLP for Computer Assisted Language Learning

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Learner Corpus Anonymization in the Age of GDPR: Insights from the Creation of a Learner Corpus of Swedish
Beáta Megyesi | Lena Granstedt | Sofia Johansson | Julia Prentice | Dan Rosén | Carl-Johan Schenström | Gunlög Sundberg | Mats Wirén | Elena Volodina
Proceedings of the 7th workshop on NLP for Computer Assisted Language Learning

2017

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Proceedings of the joint workshop on NLP for Computer Assisted Language Learning and NLP for Language Acquisition
Elena Volodina | Gintarė Grigonytė | Ildikó Pilán | Kristina Nilsson Björkenstam | Lars Borin
Proceedings of the joint workshop on NLP for Computer Assisted Language Learning and NLP for Language Acquisition

2016

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Predicting proficiency levels in learner writings by transferring a linguistic complexity model from expert-written coursebooks
Ildikó Pilán | Elena Volodina | Torsten Zesch
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

The lack of a sufficient amount of data tailored for a task is a well-recognized problem for many statistical NLP methods. In this paper, we explore whether data sparsity can be successfully tackled when classifying language proficiency levels in the domain of learner-written output texts. We aim at overcoming data sparsity by incorporating knowledge in the trained model from another domain consisting of input texts written by teaching professionals for learners. We compare different domain adaptation techniques and find that a weighted combination of the two types of data performs best, which can even rival systems based on considerably larger amounts of in-domain data. Moreover, we show that normalizing errors in learners’ texts can substantially improve classification when level-annotated in-domain data is not available.

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SweLL on the rise: Swedish Learner Language corpus for European Reference Level studies
Elena Volodina | Ildikó Pilán | Ingegerd Enström | Lorena Llozhi | Peter Lundkvist | Gunlög Sundberg | Monica Sandell
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present a new resource for Swedish, SweLL, a corpus of Swedish Learner essays linked to learners’ performance according to the Common European Framework of Reference (CEFR). SweLL consists of three subcorpora ― SpIn, SW1203 and Tisus, collected from three different educational establishments. The common metadata for all subcorpora includes age, gender, native languages, time of residence in Sweden, type of written task. Depending on the subcorpus, learner texts may contain additional information, such as text genres, topics, grades. Five of the six CEFR levels are represented in the corpus: A1, A2, B1, B2 and C1 comprising in total 339 essays. C2 level is not included since courses at C2 level are not offered. The work flow consists of collection of essays and permits, essay digitization and registration, meta-data annotation, automatic linguistic annotation. Inter-rater agreement is presented on the basis of SW1203 subcorpus. The work on SweLL is still ongoing with more that 100 essays waiting in the pipeline. This article both describes the resource and the “how-to” behind the compilation of SweLL.

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SVALex: a CEFR-graded Lexical Resource for Swedish Foreign and Second Language Learners
Thomas François | Elena Volodina | Ildikó Pilán | Anaïs Tack
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

The paper introduces SVALex, a lexical resource primarily aimed at learners and teachers of Swedish as a foreign and second language that describes the distribution of 15,681 words and expressions across the Common European Framework of Reference (CEFR). The resource is based on a corpus of coursebook texts, and thus describes receptive vocabulary learners are exposed to during reading activities, as opposed to productive vocabulary they use when speaking or writing. The paper describes the methodology applied to create the list and to estimate the frequency distribution. It also discusses some characteristics of the resulting resource and compares it to other lexical resources for Swedish. An interesting feature of this resource is the possibility to separate the wheat from the chaff, identifying the core vocabulary at each level, i.e. vocabulary shared by several coursebook writers at each level, from peripheral vocabulary which is used by the minority of the coursebook writers.

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A Report on the Automatic Evaluation of Scientific Writing Shared Task
Vidas Daudaravicius | Rafael E. Banchs | Elena Volodina | Courtney Napoles
Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications

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Coursebook Texts as a Helping Hand for Classifying Linguistic Complexity in Language Learners’ Writings
Ildikó Pilán | David Alfter | Elena Volodina
Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC)

We bring together knowledge from two different types of language learning data, texts learners read and texts they write, to improve linguistic complexity classification in the latter. Linguistic complexity in the foreign and second language learning context can be expressed in terms of proficiency levels. We show that incorporating features capturing lexical complexity information from reading passages can boost significantly the machine learning based classification of learner-written texts into proficiency levels. With an F1 score of .8 our system rivals state-of-the-art results reported for other languages for this task. Finally, we present a freely available web-based tool for proficiency level classification and lexical complexity visualization for both learner writings and reading texts.

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Proceedings of the joint workshop on NLP for Computer Assisted Language Learning and NLP for Language Acquisition
Elena Volodina | Gintarė Grigonytė | Ildikó Pilán | Kristina Nilsson Björkenstam | Lars Borin
Proceedings of the joint workshop on NLP for Computer Assisted Language Learning and NLP for Language Acquisition

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From distributions to labels: A lexical proficiency analysis using learner corpora
David Alfter | Yuri Bizzoni | Anders Agebjörn | Elena Volodina | Ildikó Pilán
Proceedings of the joint workshop on NLP for Computer Assisted Language Learning and NLP for Language Acquisition

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SweLLex: Second language learners’ productive vocabulary
Elena Volodina | Ildikó Pilán | Lorena Llozhi | Baptiste Degryse | Thomas François
Proceedings of the joint workshop on NLP for Computer Assisted Language Learning and NLP for Language Acquisition

2015

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Lark Trills for Language Drills: Text-to-speech technology for language learners
Elena Volodina | Dijana Pijetlovic
Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications

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Proceedings of the fourth workshop on NLP for computer-assisted language learning
Elena Volodina | Lars Borin | Ildikó Pilán
Proceedings of the fourth workshop on NLP for computer-assisted language learning

2014

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Reusing Swedish FrameNet for training semantic roles
Ildikó Pilán | Elena Volodina
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this article we present the first experiences of reusing the Swedish FrameNet (SweFN) as a resource for training semantic roles. We give an account of the procedure we used to adapt SweFN to the needs of students of Linguistics in the form of an automatically generated exercise. During this adaptation, the mapping of the fine-grained distinction of roles from SweFN into learner-friendlier coarse-grained roles presented a major challenge. Besides discussing the details of this mapping, we describe the resulting multiple-choice exercise and its graphical user interface. The exercise was made available through Lärka, an online platform for students of Linguistics and learners of Swedish as a second language. We outline also aspects underlying the selection of the incorrect answer options which include semantic as well as frequency-based criteria. Finally, we present our own observations and initial user feedback about the applicability of such a resource in the pedagogical domain. Students’ answers indicated an overall positive experience, the majority found the exercise useful for learning semantic roles.

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A flexible language learning platform based on language resources and web services
Elena Volodina | Ildikó Pilán | Lars Borin | Therese Lindström Tiedemann
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present Lärka, the language learning platform of Spräkbanken (the Swedish Language Bank). It consists of an exercise generator which reuses resources available through Spräkbanken: mainly Korp, the corpus infrastructure, and Karp, the lexical infrastructure. Through Lärka we reach new user groups ― students and teachers of Linguistics as well as second language learners and their teachers ― and this way bring Spräkbanken’s resources in a relevant format to them. Lärka can therefore be viewed as an case of real-life language resource evaluation with end users. In this article we describe Lärka’s architecture, its user interface, and the five exercise types that have been released for users so far. The first user evaluation following in-class usage with students of linguistics, speech therapy and teacher candidates are presented. The outline of future work concludes the paper.

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Rule-based and machine learning approaches for second language sentence-level readability
Ildikó Pilán | Elena Volodina | Richard Johansson
Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications

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Proceedings of the third workshop on NLP for computer-assisted language learning
Elena Volodina | Lars Borin | Ildikó Pilán
Proceedings of the third workshop on NLP for computer-assisted language learning

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You Get what You Annotate: A Pedagogically Annotated Corpus of Coursebooks for Swedish as a Second Language
Elena Volodina | Ildikó Pilán | Stian Rødven Eide | Hannes Heidarsson
Proceedings of the third workshop on NLP for computer-assisted language learning

2012

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Introducing the Swedish Kelly-list, a new lexical e-resource for Swedish
Elena Volodina | Sofie Johansson Kokkinakis
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Frequency lists and/or lexicons contain information about the words and their statistics. They tend to find their “readers” among language learners, language teachers, linguists and lexicographers. Making them available in electronic format helps to expand the target group to cover language engineers, computer programmers and other specialists working in such areas as information retrieval, spam filtering, text readability analysis, test generation etc. This article describes a new freely available electronic frequency list of modern Swedish which was created in the EU project KELLY. We provide a short description of the KELLY project; examine the methodological approach and mention some details on the compiling of the corpus from which the list has been derived. Further, we discuss the type of information the list contains; describe the steps for list generation; provide information on the coverage and some other statistics over the items in the list. Finally, some practical information on the license for the Swedish Kelly-list distribution is given; potential application areas are suggested; and future plans for its expansion are mentioned. We hope that with some publicity we can help this list find its users.