Automatic extraction of cognate matrices from BabelNet yields sparse, noisy data with GQ distances above 0.39 from the Glottolog gold standard, confirming a data bottleneck for computational historical linguistics.
Phonetic Word Embeddings
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abstract
This work presents a novel methodology for calculating the phonetic similarity between words taking motivation from the human perception of sounds. This metric is employed to learn a continuous vector embedding space that groups similar sounding words together and can be used for various downstream computational phonology tasks. The efficacy of the method is presented for two different languages (English, Hindi) and performance gains over previous reported works are discussed on established tests for predicting phonetic similarity. To address limited benchmarking mechanisms in this field, we also introduce a heterographic pun dataset based evaluation methodology to compare the effectiveness of acoustic similarity algorithms. Further, a visualization of the embedding space is presented with a discussion on the various possible use-cases of this novel algorithm. An open-source implementation is also shared to aid reproducibility and enable adoption in related tasks.
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The Cognate Data Bottleneck in Language Phylogenetics
Automatic extraction of cognate matrices from BabelNet yields sparse, noisy data with GQ distances above 0.39 from the Glottolog gold standard, confirming a data bottleneck for computational historical linguistics.