AI RESEARCH
NormEval: A Unified Multi-Metric Framework for Evaluating Semantic Fidelity in Text Normalization
arXiv CS.CL
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ArXi:2511.20409v2 Announce Type: replace Text normalization methods such as stemming and lemmatization are fundamental components of NLP pipelines. As new normalization tools are developed for diverse languages, evaluation methodologies remain fragmented, relying on Compression Ratio, downstream accuracy, or sequence-to-sequence prediction scores in isolation, failing to distinguish between beneficial vocabulary reduction and harmful semantic distortion.