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%.

Authors

• Doug Downey
• Matthew Broadhead
• Oren Etzioni

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