Skip to contents

Extract Named Entities from Tokens

Source: R/lexical_analysis.R
extract_named_entities.Rd

Uses spaCy to extract named entities (NER) from tokenized text. Returns a data frame with token-level entity annotations.

Usage

extract_named_entities(
  tokens,
  include_pos = TRUE,
  include_lemma = TRUE,
  model = "en_core_web_sm"
)

Arguments

tokens

A quanteda tokens object or character vector of texts.

include_pos

Logical; include POS tags (default: TRUE).

include_lemma

Logical; include lemmatized forms (default: TRUE).

model

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

Value

A data frame with columns:

  • doc_id: Document identifier

  • token: Original token

  • entity: Named entity type (e.g., PERSON, ORG, GPE)

  • pos: Universal POS tag (if include_pos = TRUE)

  • lemma: Lemmatized form (if include_lemma = TRUE)

Details

This function requires the spacyr package and a working Python environment with spaCy installed. If spaCy is not initialized, this function will attempt to initialize it with the specified model.

See also

Other lexical: calculate_text_readability(), clear_lexdiv_cache(), detect_multi_words(), extract_keywords_keyness(), extract_keywords_tfidf(), extract_morphology(), extract_pos_tags(), lexical_analysis, lexical_diversity_analysis(), lexical_frequency_analysis(), 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(), summarize_morphology()

Examples

if (FALSE) { # \dontrun{
tokens <- quanteda::tokens("Apple Inc. was founded by Steve Jobs in California.")
entity_data <- extract_named_entities(tokens)
print(entity_data)
} # }