Lemmatize Texts with spaCy

Perform lemmatization using spaCy with optimized pipeline settings. Disables unnecessary components (NER, parser) for faster processing.

Usage

spacy_lemmatize(x, batch_size = 100, model = "en_core_web_sm")

Arguments

x

Character vector of texts OR a quanteda tokens object.

batch_size

Integer; batch size for processing (default: 100).

model

Character; spaCy model to use (default: "en_core_web_sm").

Value

A data frame with columns: doc_id, token_id, token, lemma.

Details

This function disables NER, entity_ruler, and parser components to speed up lemmatization. Use this when you need lemmas without other annotations.

See also

Other lexical: calculate_dispersion_metrics(), calculate_lexical_dispersion(), calculate_log_odds_ratio(), calculate_text_readability(), clear_lexdiv_cache(), detect_multi_words(), extract_keywords_keyness(), extract_keywords_tfidf(), extract_morphology(), extract_named_entities(), extract_noun_chunks(), extract_pos_tags(), extract_subjects_objects(), find_similar_words(), get_sentences(), get_spacy_embeddings(), get_spacy_model_info(), get_word_similarity(), init_spacy_nlp(), lexical_analysis, lexical_diversity_analysis(), lexical_frequency_analysis(), parse_morphology_string(), plot_keyness_keywords(), plot_keyword_comparison(), plot_lexical_diversity_distribution(), plot_morphology_feature(), plot_readability_by_group(), plot_readability_distribution(), plot_tfidf_keywords(), plot_top_readability_documents(), render_displacy_dep(), render_displacy_ent(), spacy_extract_entities(), spacy_has_vectors(), spacy_initialized(), spacy_parse_full(), summarize_morphology()