text-processing

Ruby's regex engine provides powerful text processing. I use =~ for matching, match for captures. Character classes \d, \w, \s match digits, words, whitespace. Quantifiers *, +, ?, {n,m} control repetition. Anchors ^ and $ match start/end. Groups () c

I like spaCy for production NLP because it balances performance, ergonomics, and deployability. It is especially good for entity extraction, rule-based matching, and clean token-level processing. I often pair learned models with explicit match pattern

Regex is not glamorous, but it remains one of the fastest ways to turn messy text into useful structured fields. I use it for IDs, dates, codes, and log fragments before reaching for heavier NLP. The important part is making patterns specific enough t