---
title: "Locating Complex Named Entities in Web Text"
authors:
  - "Doug Downey"
  - "Matthew Broadhead"
  - "Oren Etzioni"
doi: ""
url: "https://ijcai.org/proceedings/2007/439"
pdf: "https://ijcai.org/proceedings/2007/0439.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "2733-2738"
track: ""
keywords:
---

# Locating Complex Named Entities in Web Text

Doug Downey, Matthew Broadhead, Oren Etzioni

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 2733-2738.

## Abstract

Named Entity Recognition (NER) is the task of locating and classifying names in text. In previous work, NER was limited to a small number of pre-defined entity classes (e.g., people, locations, and organizations). However, NER on the Web is a far more challenging problem. Complex names (e.g., film or book titles) can be very difficult to pick out precisely from text. Further, the Web contains a wide variety of entity classes, which are not known in advance. Thus, hand-tagging examples of each entity class is impractical. This paper investigates a novel approach to the first step in Web NER: locating complex named entities in Web text. Our key observation is that named entities can be viewed as a species of multi-word units, which can be detected by accumulating n-gram statistics over the Web corpus. We show that this statistical method's F1 score is 50% higher than that of supervised techniques including Conditional Random Fields (CRFs) and Conditional Markov Models (CMMs) when applied to complex names. The method also outperforms CMMs and CRFs by 117% on entity classes absent from the training data. Finally, our method outperforms a semi-supervised CRF by 73%.