extract_entities_workflow: Extract entities from text with improved efficiency using...

In LBDiscover: Literature-Based Discovery Tools for Biomedical Research

Extract entities from text with improved efficiency using only base R

Description

This function provides a complete workflow for extracting entities from text using dictionaries from multiple sources, with improved performance and robust error handling.

Usage

extract_entities_workflow(
  text_data,
  text_column = "abstract",
  entity_types = c("disease", "drug", "gene"),
  dictionary_sources = c("local", "mesh", "umls"),
  additional_mesh_queries = NULL,
  sanitize = TRUE,
  api_key = NULL,
  custom_dictionary = NULL,
  max_terms_per_type = 200,
  verbose = TRUE,
  batch_size = 500,
  parallel = FALSE,
  num_cores = 2,
  cache_dictionaries = TRUE
)

Arguments

text_data	A data frame containing article text data.
text_column	Name of the column containing text to process.
entity_types	Character vector of entity types to include.
dictionary_sources	Character vector of sources for entity dictionaries.
additional_mesh_queries	Named list of additional MeSH queries.
sanitize	Logical. If TRUE, sanitizes dictionaries before extraction.
api_key	API key for UMLS access (if "umls" is in dictionary_sources).
custom_dictionary	A data frame containing custom dictionary entries to incorporate into the entity extraction process.
max_terms_per_type	Maximum number of terms to fetch per entity type. Default is 200.
verbose	Logical. If TRUE, prints detailed progress information.
batch_size	Number of documents to process in a single batch. Default is 500.
parallel	Logical. If TRUE, uses parallel processing when available. Default is FALSE.
num_cores	Number of cores to use for parallel processing. Default is 2.
cache_dictionaries	Logical. If TRUE, caches dictionaries for faster reuse. Default is TRUE.

Value

A data frame with extracted entities, their types, and positions.

LBDiscover documentation built on June 16, 2025, 5:09 p.m.