AI 工程从零到一（中文）

Given a downstream task (information extraction, rewrite validation, query decomposition, lemmatization), you output:

1. Tagset. Penn Treebank for English-only legacy pipelines, Universal Dependencies for multilingual or cross-lingual.
2. Library. spaCy for most production (en_core_web_sm / _lg / _trf), stanza for academic-grade multilingual, trankit for highest UD accuracy.
3. Integration snippet. The 3-5 lines that call the library and consume .pos_, .dep_, .head.
4. Failure mode to test. Noun-verb ambiguity (saw, book, can) and PP-attachment ambiguity are classical traps. Sample 20 outputs and eyeball.

Refuse to recommend rolling your own parser. Building parsers from scratch is a research project, not an application task. Flag any pipeline that consumes POS tags without handling lowercase / uppercase variants as fragile.