"Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories..."
The process of identifying and categorizing named entities in text, such as names of people, organizations, and locations.
Natural Language Processing (NLP): This is a subfield of computer science and linguistics that focuses on enabling computers to understand and process human language.
Semantics: This refers to the study of meaning in language, and how words and phrases convey information and context.
Named Entity Recognition (NER): This is the task of identifying and extracting specific entities from text, such as people, locations, organizations, and dates.
Parts of Speech Tagging: This is the process of labeling the parts of speech in a sentence, such as nouns, verbs, adjectives, and adverbs, which can help identify specific entities.
Chunking: This is the process of grouping words together in a sentence based on their part of speech, which can help identify larger entities.
Linguistic features: These are characteristics of language, such as grammar, syntax, and semantics, that can be used to identify specific entities.
Machine learning: This is the field of study concerned with developing algorithms and models that can learn patterns in data and make predictions or classifications.
Training data: This is data that is used to train machine learning models, such as sentences labeled with entity type annotations.
Evaluation metrics: These are measures used to evaluate the performance of NER models, such as precision, recall, and F1 score.
Named Entity Recognition algorithms: These are specific algorithms and techniques used to perform NER, such as rule-based methods, statistical models, and deep learning models.
Person: Recognizes names of people, both real and fictional.
Organization: Recognizes names of companies, institutions, and other organizations.
Location: Recognizes names of cities, countries, regions, landmarks, and other geographic locations.
Date: Recognizes dates in various formats, including personal dates, historical dates, and chronologies.
Time: Recognizes times in different formats, including clock time and duration.
Money: Recognizes amount in various currencies, as well as references to financial indicators.
Percent: Recognizes percentages of numbers.
Product: Recognizes names of consumer products, including brand names, models, and types.
Event: Recognizes names of events, including meetings, conferences, and sporting events.
Chemical: Recognizes names of chemical compounds, including scientific and common names.
Medical: Recognizes names of medical conditions, procedures, and treatments.
Music: Recognizes names of musical compositions, albums, and artists.
Art: Recognizes names of visual art, including artists, artwork, and styles.
Religion: Recognizes names of religious figures, institutions, and practices.
Education: Recognizes names of educational institutions, degrees, and programs.
Law: Recognizes names of legal entities, concepts, and procedures.
Language: Recognizes names of languages and language families.
Sports: Recognizes names of sports, teams, and athletes.
"...into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc."
"Most research on NER/NEE systems has been structured as taking an unannotated block of text... and producing an annotated block of text that highlights the names of entities."
"Taking an unannotated block of text, such as this one..."
"[Jim]Person bought 300 shares of [Acme Corp.]Organization in [2006]Time."
"A person name consisting of one token, a two-token company name, and a temporal expression have been detected and classified."
"For example, the best system entering MUC-7 scored 93.39% of F-measure while human annotators scored 97.60% and 96.95%."
"Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction)..."
"...that seeks to locate and classify named entities mentioned in unstructured text..."
"...person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc."
"...to locate and classify named entities..."
"...the best system entering MUC-7 scored 93.39% of F-measure while human annotators scored 97.60% and 96.95%."
"[2006]Time."
"...(also known as (named) entity identification, entity chunking, and entity extraction)..."
"...that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories..."
"[Jim]Person bought 300 shares of [Acme Corp.]Organization in [2006]Time."
"The best system entering MUC-7 scored 93.39% of F-measure..."
"...while human annotators scored 97.60% and 96.95%."
"Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction..."
"...named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc."