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Register a new userUnsupervised Paradigm Clustering Using Transformation Rules
تجميع النماذج غير الخاضعة لها باستخدام قواعد التحول
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تصف هذه الورقة تقديم فريق CU-UBC لمهمة SIGMORPHON 2021 المشتركة 2: تجميع النماذج المورفولوجية غير المنصوص عليها.يولد نظامنا النماذج باستخدام قواعد التحول المورفولوجية التي يتم اكتشافها من البيانات الأولية.نقوم بتجربة طريقتين لاكتشاف القواعد.نهجنا الأول يولد بادئة وتحولات لاحقة بين سلاسل مماثلة.ثانيا، نحن نقوم بتجربة المزيد من القواعد العامة التي يمكن أن تطبق التحولات داخل سلاسل الإدخال بالإضافة إلى التحويلات البادئة واللاحقة.نجد أن أفضل أداء إجمالي يتم تسليمها عن طريق البادئة وقواعد اللاحقة ولكن المزيد من قواعد التحول العامة تؤدي أفضل لغات مع التشكل الغزيرة ونباتات مورفيم إلى كلمة عالية للغاية.
This paper describes the submission of the CU-UBC team for the SIGMORPHON 2021 Shared Task 2: Unsupervised morphological paradigm clustering. Our system generates paradigms using morphological transformation rules which are discovered from raw data. We experiment with two methods for discovering rules. Our first approach generates prefix and suffix transformations between similar strings. Secondly, we experiment with more general rules which can apply transformations inside the input strings in addition to prefix and suffix transformations. We find that the best overall performance is delivered by prefix and suffix rules but more general transformation rules perform better for languages with templatic morphology and very high morpheme-to-word ratios.
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This paper presents two different systems for unsupervised clustering of morphological paradigms, in the context of the SIGMORPHON 2021 Shared Task 2. The goal of this task is to correctly cluster words in a given language by their inflectional parad
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ta. We employ agglomerative clustering to group word forms together using a metric that combines an orthographic distance and a semantic distance from word embeddings. We experiment with two variations of an edit distance-based model for quantifying orthographic distance, but, due to time constraints, our system does not improve over the shared task's baseline system.
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