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This work describes an implementation of the "extended alignment" (or"multitiers") approach for cognate reflex prediction, submitted to "Predictionof Cognate Reflexes" shared task. Similarly to List2022d, the techniqueinvolves an automatic extension of sequence alignments with multilayeredvectors that encode informational tiers on both site-specific traits, such assound classes and distinctive features, as well as contextual andsuprasegmental ones, conveyed by cross-site referrals and replication. Themethod allows to generalize the problem of cognate reflex prediction as aclassification problem, with models trained using a parallel corpus of cognatesets. A model using random forests is trained and evaluated on the shared taskfor reflex prediction, and the experimental results are presented and discussedalong with some differences to other implementations.

Approaching Reflex Predictions as a Classification Problem Using Extended Phonological Alignments