Methods & evaluation
How NLP systems turn text into something a model can use, and how to tell whether they actually work: tokenization, word senses, benchmarks and machine translation metrics.
- How NLP Models Are Evaluated: Benchmarks, Metrics and Their Blind Spots
A practical guide to evaluating natural language processing systems: test sets and splits, accuracy, precision, recall and F1, benchmark suites such as GLUE and HELM, significance testing, annotation artefacts and data contamination.
- How Tokenization Works: Words, Subwords and Bytes in NLP
A clear guide to tokenization in natural language processing: why models split text into subword units, how BPE, WordPiece, unigram and SentencePiece differ, why some languages cost more tokens than others, and what to check in practice.
- Machine Translation Evaluation: BLEU, chrF, COMET and Human Judgement
How machine translation quality is measured: BLEU and its pitfalls, chrF, learned metrics such as COMET, direct assessment and MQM human evaluation, quality estimation without references, and how to compare two systems fairly.
- Word Sense Disambiguation: How Machines Decide What a Word Means
What word sense disambiguation is, from Lesk's dictionary overlaps to contextual embeddings, how it is evaluated with WordNet, SemCor and the Senseval/SemEval benchmarks, and why ambiguity still trips up translation and search.
