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Extract Named Entities with spaCy

Source: R/lexical_analysis.R
spacy_extract_entities.Rd

Extract named entities from texts using spaCy NER.

Usage

spacy_extract_entities(x, model = "en_core_web_sm")

Arguments

x

Character vector of texts OR a quanteda tokens object.

model

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

Value

A data frame with entity information:

  • doc_id: Document identifier

  • text: Entity text

  • label: Entity type (PERSON, ORG, GPE, etc.)

  • start_char: Start character position

  • end_char: End character position

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_has_vectors(), spacy_initialized(), spacy_parse_full(), summarize_morphology()