Yousuf Ali Mohammed

Also published as: Yousuf Ali Mohammed


2021

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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

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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.

2020

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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.