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name: word-and-lexical-tools description: "Use TextBlob Word and WordList for word-level morphology, spelling correction, lemmatization, WordNet lookup, and lexical frequency utilities." disable-model-invocation: true metadata: disco-role: operating license: MIT

TextBlob word and lexical tools

Use this sub-skill when the task is word-level: inflecting words, stemming, lemmatizing with optional POS tags, checking spelling suggestions, using WordNet synsets/definitions, operating on WordList, or counting already extracted words/noun phrases.

Route elsewhere when the user needs document-level NLP first:

• Tokenization, sentence splitting, POS tagging, noun-phrase extraction, sentiment, parsing, n-grams, or full TextBlob workflows: sibling sub-skill core-nlp-workflows.
• Classifier training, classifier data formats, feature extractors, or TextBlob(..., classifier=...): sibling sub-skill classifiers-and-data-formats.

Bundled resources

• Lexical reference: API details, recipes, WordNet POS mapping, WordList behavior, and frequency patterns.
• Troubleshooting: missing corpora, wrong lemmas, spelling-correction caveats, inflection surprises, and list mutation gotchas.
• Lexical smoke script: deterministic installed package smoke checks. Run python scripts/lexical_smoke.py --help.

Operating workflow

1. Confirm this is word-level. If raw text still needs tokenization, POS tagging, noun phrases, or sentiment, route to core-nlp-workflows first and return here for lexical normalization or counts on the resulting words.
2. Create lexical objects. Import Word and WordList from textblob: Word(string, pos_tag=None) for one token and WordList(collection) for a list-like token collection.
3. Choose the lexical operation.
   • Inflection: Word(...).singularize() and .pluralize(); batch with WordList(...).singularize() or .pluralize().
   • Lemmatization: Word(...).lemmatize(pos=None) or cached .lemma when a Word was created with pos_tag; pass a WordNet or Penn POS for verbs, adjectives, and adverbs.
   • Stemming: Word(...).stem() or WordList(...).stem(); Porter is the default stemmer.
   • Spelling: Word(...).spellcheck() returns (candidate, confidence) tuples; .correct() returns the top candidate as a Word.
   • WordNet: .synsets, .get_synsets(pos=...), .definitions, and .define(pos=...) require the NLTK WordNet corpus.
   • Counts: use WordList.count(term, case_sensitive=False) for words or noun phrases that have already been extracted.
4. Check corpora before WordNet-dependent operations. lemmatize, lemma, synsets, and definitions rely on NLTK WordNet data. Do not download corpora implicitly; use the smoke script with --skip-wordnet when corpus-backed checks should be bypassed.
5. Record uncertainty. Spelling correction is heuristic and approximate; keep the original token, the proposed correction, and confidence when normalizing noisy keywords.
6. Troubleshoot near the workflow. If output is surprising, inspect references/troubleshooting.md before changing algorithms.

Quick patterns

from textblob import Word, WordList
from textblob.wordnet import VERB

Word("cats").singularize()          # Word('cat')
Word("speling").correct()           # Word('spelling')
Word("went").lemmatize(VERB)        # 'go'
Word("went", "VBD").lemma          # 'go'
WordList(["Dog", "dog"]).count("dog")  # 2, case-insensitive by default

For noisy keyword normalization, combine spelling suggestions with an explicit POS-aware lemma and keep audit fields:

from textblob import Word
from textblob.wordnet import NOUN

def normalize_keyword(token, pos=NOUN, min_confidence=0.80):
    suggestion, confidence = Word(token).spellcheck()[0]
    chosen = suggestion if confidence >= min_confidence else token
    return {
        "original": token,
        "suggestion": suggestion,
        "confidence": confidence,
        "normalized": Word(chosen).lemmatize(pos),
    }

If Word("went").lemmatize() returns "went", it used the default noun POS; recover with Word("went").lemmatize("v"), Word("went").lemmatize(VERB), or Word("went", "VBD").lemma.