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name: grammar-parse-semantics description: "Use NLTK grammars, parsers, chunkers, trees, dependency graphs, semantic logic, and inference APIs without reopening the source repo." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Grammar Parse Semantics

Use this sub-skill when the task is about NLTK grammar authoring, parse-tree generation or inspection, regular-expression chunking, dependency graph handling, semantic interpretation, logic expressions, or theorem-proving/model-building wrappers.

Route away from this sub-skill when the task is mainly about:

• Installing or locating NLTK data packages, sample grammars, corpora, or model resources: use ../data-and-downloader/SKILL.md first.
• Tokenization, POS tagging, stemming, lemmatization, or VADER preprocessing before parsing/chunking: use ../tokenize-tag-stem/SKILL.md when available.
• Classifiers, language models, probability distributions, metrics, or translation/alignment: use ../ml-metrics-and-translation/SKILL.md.

Fast Paths

• For small grammar workflows, prefer in-memory CFG.fromstring(...), PCFG.fromstring(...), or FeatureGrammar.fromstring(...); then parse already-tokenized input with ChartParser, RecursiveDescentParser, ShiftReduceParser, ViterbiParser, or feature chart parsers.
• When a parser returns no trees, first check exact terminal/token alignment with grammar.check_coverage(tokens), casing, punctuation, and the grammar start symbol before changing parser strategy.
• For ambiguity, use ChartParser or chart strategy subclasses and count/inspect all returned trees. Use ShiftReduceParser only when a single heuristic parse is acceptable; it can miss parses in ambiguous grammars.
• For chunking, feed RegexpParser POS-tagged tuples such as [('the', 'DT'), ('dog', 'NN')]; chunk grammars operate over tags, not raw words.
• For tree manipulation, use Tree.fromstring, label(), leaves(), treepositions(), subtrees(), productions(), pformat(), chomsky_normal_form(), and collapse_unary() rather than manually traversing nested lists.
• For dependency input, start with a tiny Malt/CoNLL string and DependencyGraph; use DependencyEvaluator only after verifying equal token sequences.
• For semantics, parse expressions with Expression.fromstring, evaluate with Valuation, Assignment, and Model, and use FeatureGrammar SEM features when connecting parse trees to semantic representations.
• Treat CoreNLP, Stanford, Malt, BLLIP, Prover9, Mace4, Graphviz rendering, and trained transition-parser models as optional external-wrapper workflows. They require external services, Java/JARs, binaries, models, or trusted model files and are not part of the base no-download path.

Reference Map

• Public grammar, parser, chunker, tree, dependency, semantics, inference, and optional wrapper API contracts: references/api-reference.md.
• Task recipes for CFG/PCFG/feature grammars, parser comparisons, chunking, tree transforms, dependency graphs, semantic models, DRT, and wrapper caveats: references/workflows.md.
• Failure diagnosis for empty parses, malformed grammars, chunk regexes, tree parsing, dependency files, semantic logic, resource bounds, and optional wrappers: references/troubleshooting.md.
• Tiny in-memory no-download runtime check covering CFG/chart/RD/shift-reduce parsing, PCFG/Viterbi, feature grammar semantics, chunking, trees, dependency graphs, logic/model evaluation, and DRT: scripts/grammar_parse_smoke.py.

Minimum Validation Pattern

1. Print nltk.__version__, Python version, and the parser or grammar class being used.
2. Keep tokenization/tagging explicit; for CFG/PCFG parsers, assert grammar.check_coverage(tokens) succeeds before interpreting an empty parse list.
3. For ambiguity-sensitive tasks, report parse count and the parser strategy; do not silently take the first tree unless the task says one-best is acceptable.
4. For RegexpParser, assert input is POS-tagged, run tree2conlltags() or inspect Tree subtrees, and document whether nested chunks are expected.
5. For semantic workflows, assert the parsed expression, valuation domain, assignment, and Model.evaluate(...) result; handle 'Undefined' separately from False.
6. For external parser/prover wrappers, verify the exact external binary/JAR/service/model path and timeout/resource settings before invoking them.
7. Run the bundled no-download smoke from any current working directory in an environment with NLTK installed:

python /path/to/skills/disco/nltk/sub-skills/grammar-parse-semantics/scripts/grammar_parse_smoke.py --help
python /path/to/skills/disco/nltk/sub-skills/grammar-parse-semantics/scripts/grammar_parse_smoke.py --json