Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons

This work has shown that conditionally-trained models, such as conditional maximum entropy models, handle inter-dependent features of greedy sequence modeling in NLP well.

Abstract

Models for many natural language tasks benefit from the flexibility to use overlapping, non-independent features. For example, the need for labeled data can be drastically reduced by taking advantage of domain knowledge in the form of word lists, part-of-speech tags, character n-grams, and capitalization patterns. While it is difficult to capture such inter-dependent features with a generative probabilistic model, conditionally-trained models, such as conditional maximum entropy models, handle them well. There has been significant work with such models for greedy sequence modeling in NLP (Ratnaparkhi, 1996; Borthwick et al., 1998).