What problem does it solve?
This Skill helps you turn raw text corpora into measurable linguistic insights, like frequencies, key keywords, collocations, and concordance lines, so you can analyze language use quantitatively instead of manually.
Core Features & Use Cases
- Corpus preprocessing with NLTK + spaCy: tokenization, optional lemmatization, POS tagging support, and named-entity extraction using spaCy pipelines.
- Descriptive frequency analysis: unigram/bigram/trigram counts, type-token ratio, hapax legomena, and vocabulary statistics.
- Key word and association discovery: log-likelihood keyword analysis against a reference corpus plus collocation scoring (PMI / log-likelihood / t-score) to surface meaningful word pairings.
- KWIC concordance (Key Word In Context): produce context windows around a chosen keyword to inspect usage patterns.
- Distributional semantics (word2vec): train word2vec models and compute cosine similarity to compare semantic neighborhoods.
- Diachronic-ready workflows: structure outputs (per year/decade/partition) to track how lexical patterns and associations change over time.
Quick Start
Use the corpus-linguistics skill to preprocess your texts, then compute keywords and KWIC for a target term you care about against a reference corpus.
