Named entity recognition

From Wikipedia, the free encyclopedia

Named entity recognition (NER) (also known as entity identification (EI) and entity extraction) is a subtask of information extraction that seeks to locate and classify atomic elements in text into predefined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.

For example, a NER system producing MUC-style output might tag the sentence,

Jim bought 300 shares of Acme Corp. in 2006.

<ENAMEX TYPE="PERSON">Jim</ENAMEX> bought <NUMEX TYPE="QUANTITY">300</NUMEX> shares of <ENAMEX TYPE="ORGANIZATION">Acme Corp.</ENAMEX> in <TIMEX TYPE="DATE">2006</TIMEX>.

NER systems have been created that use linguistic grammar-based techniques as well as statistical models. Hand-crafted grammar-based systems typically obtain better results, but at the cost of months of work by experienced linguists. Statistical NER systems typically require a large amount manually annotated training data, but can be ported to other languages, domains or genres of text much more rapidly and require less work overall.

Since about 1998, there has been a great deal of interest in entity identification in the molecular biology, bioinformatics, and medical natural language processing communities. The most common entity of interest in that domain has been names of genes and gene products.

1 Named entity types
2 Evaluation
3 External links

[edit] Named entity types

In the expression named entity, the word named restricts the task to those entities for which one or many rigid designators, as defined by Kripke, stands for the referent. For instance, the automotive company created by Henry Ford in 1903 is referred to as Ford or Ford Motor Company. Rigid designators include proper names as well as certain natural kind terms like biological species and substances.

There is a general agreement to include temporal expressions and some numerical expressions such as money and measures in named entities. While some instances of these types are good examples of rigid designators (e.g., the year 2001) there are also many invalid ones (e.g., I take my vacations in “June”). In the first case, the year 2001 refers to the 2001st year of the Gregorian calendar. In the second case, the month June may refer to the month of an undefined year (past June, next June, June 2020, etc.). It is arguable that the named entity definition is loosened in such cases for practical reasons.

At least two hierarchies of named entity types have been proposed in the literature. BBN categories [1], proposed in 2002, is used for Question answering and consists of 29 types and 64 subtypes. Sekine's extended hierarchy [2], proposed in 2002, is made of 200 subtypes.

[edit] Evaluation

Benchmarking and evaluations have been performed in the Message Understanding Conferences (MUC) organized by DARPA, International Conference on Language Resources and Evaluation (LREC), Computational Natural Language Learning (CoNLL) workshops, Automatic Content Extraction (ACE) organized by NIST, the Multilingual Entity Task Conference (MET), Information Retrieval and Extraction Exercise (IREX) and in HAREM (Portuguese language only).

[edit] External links

[edit] Evaluation forums

[edit] Datasets and hierarchies

[edit] Software

LingPipe Java Natural Language Processing software that includes a trainable named-entity extraction framework with first-best, n-best and confidence-ranked-by-entity output. Models available for various languages and genres. See the online demos.
Named Entity Tagger Yet another demo system for named entity tagging. Allows users to enter their own text.
Balie Baseline implementation of named entity recognition.
ANNIE Information extraction package (a GATE component) with NER capabilities.
MinorThird Collection of Java classes for storing text, annotating text, and learning to extract entities and categorize text.
ABNER Biomedical named entity recognizer.
POSBIOTM/W NER client tool that enables users to automatically annotate biomedical-related entities.
ISYS An enterprise search product which includes automatic entity recognition
ESpotter A domain and user adaptation approach for named entity recognition on the Web.
FreeLing An open source language analysis tool suite. See the online demo.
ClearForest Commercial natural language processing toolkit that includes NER. See the online demo.
PolyAnalyst Commercial natural language processing suite with entity extraction tools
SRA NetOwl Commercial and state-of-the-art recognizer in its class (rule and statistical based) covering many scripts and including highly inflected languages such as Arabic.
Inxight Software Commercial natural language processing, entity extraction and fact extraction in 32 languages.
Trifeed Ltd. Trifeed is a research and development software company operating in the field of text analysis and information extraction. See the online demo.