A_survey_of_named_entity_recognition_and_classification.pdf
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A survey of named entity recognition and classification
David Nadeau, Satoshi Sekine
National Research Council Canada / New York University
Introduction
The term “Named Entity”, now widely used in Natural Language Processing, was coined
for the Sixth Message Understanding Conference (MUC-6) (R. Grishman & Sundheim
1996). At that time, MUC was focusing on Information Extraction (IE) tasks where
structured information of company activities and defense related activities is extracted
from unstructured text, such as newspaper articles. In defining the task, people noticed
that it is essential to recognize information units like names, including person,
organization and location names, and numeric expressions including time, date, money
and percent expressions. Identifying references to these entities in text was recognized as
one of the important sub-tasks of IE and was called “Named Entity Recognition and
Classification (NERC)”.
We present here a survey of fifteen years of research in the NERC field, from 1991
to 2006. While early systems were making use of handcrafted rule-based algorithms,
modern systems most often resort to machine learning techniques. We survey these
techniques as well as other critical aspects of NERC such as features and evaluation
methods. It was indeed concluded in a recent conference that the choice of features is at
least as important as the choice of technique for obtaining a good NERC system (E.
Tjong Kim Sang & De Meulder 2003). Moreover, the way NERC systems are evaluated
and compared is essential to progress in the field. To the best of our knowledge, NERC
features, techniques, and evaluation methods have not been surveyed extensively yet.
The first section of this survey presents some observations on published work from
the point of view of activity per year, supported languages, preferred textual genre and
domain, and supported entity types. It was collected from the review of a hundred
English language papers sampled from the major conferences and journals. We do not
claim this review to be exhaustive or representative of all the research in all languages,
but we believe it gives a good feel for the breadth and depth of previous work. Section 2
covers the algorithmic techniques that were proposed for addressing the NERC task.
Most techniques are borrowed from the Machine Learning (ML) field. Instead of
elaborating on techniques themselves, the third section lists and classifies the proposed
features, i.e., descriptions and characteristic of words for algorithmic consumption.
Section 4 presents some of the evaluation paradigms that were proposed throughout the
major forums. Finally, we present our conclusions.
1 Observations: 1991 to 2006
The computational research aiming at automatically identifying named entities in texts
forms a vast and heterogeneous pool of strategies, methods and representations. One of
the first research papers in the field was presented by Lisa F. Rau (1991) at the Seventh
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