Named Entity Recognition: A Maximum Entropy Approach Using Global Information

By Hai Leong Chieu, Hwee Tou Ng. International Conference on Computational Linguistics, 2002

337 citations recorded by the source corpus.

This paper presents a maximum entropy-based named entity recognizer (NER). It differs from previous machine learning-based NERs in that it uses information from the whole document to classify each word, with just one classifier. Previous work that involves the gathering of information from the whole document often uses a secondary classifier, which corrects the mistakes of a primary sentence-based classifier. In this paper, we show that the maximum entropy framework is able to make use of global information directly, and achieves performance that is comparable to the best previous machine learning-based NERs on MUC-6 and MUC-7 test data.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/96752dc3a8c3edff8ef69b9c0fdeff9bde2b48f1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Molecular Biology

Citations in the corpus

GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling (2019, background): “Exploit Global Information Chieu and Ng (2002) explore the usage of global feature in the whole document by the co-occurrence of each token, which is fed into a maximum entropy classifier.”
Visual Attention Model for Name Tagging in Multimodal Social Media (2018, background): “…2016; Ma and Hovy, 2016) proposed several neural network architectures for named tagging that outperform traditional explicit features based methods (Chieu and Ng, 2002; Florian et al., 2003; Ando and Zhang, 2005; Ratinov and Roth, 2009; Lin and Wu, 2009; Passos et al., 2014; Luo et al., 2015).”
Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer (2020, neutral): “…features, followed by feeding them to different linear classi-ﬁers such as maximum entropy, conditional random ﬁelds (CRFs), and support vector machines (Chieu and Ng, 2002; Florian et al., 2003; Finkel et al., 2005; Ratinov and Roth, 2009; Lin and Wu, 2009; Passos et al., 2014; Luo et al., 2015).”
Empower Sequence Labeling with Task-Aware Neural Language Model (2017, methodology): “Handcrafted features were widely used in traditional methods like CRFs, HMMs, and maximum entropy classifiers (Lafferty, McCallum, and Pereira 2001; McCallum and Li 2003; Florian et al. 2003; Chieu and Ng 2002), but also make it hard to apply them to new tasks or domains.”
Learning to Automatically Solve Logic Grid Puzzles (2015, methodology): “Maximum entropy classifier has been successfully applied in many natural language processing applications (Charniak, 2000; Chieu and Ng, 2002; Ratnaparkhi and others, 1996) and allows the inclusion of various sources of information without necessarily assuming any independence between the features.”
Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling (2005, background): “Chieu and Ng (2002) propose a solution to this problem: for each token, they define additional features taken from other occurrences of the same token in the document.”