Efficient Support Vector Classifiers for Named Entity Recognition

By Hideki Isozaki, Hideto Kazawa. International Conference on Computational Linguistics, 2002

387 citations recorded by the source corpus.

Named Entity (NE) recognition is a task in which proper nouns and numerical information are extracted from documents and are classified into categories such as person, organization, and date. It is a key technology of Information Extraction and Open-Domain Question Answering. First, we show that an NE recognizer based on Support Vector Machines (SVMs) gives better scores than conventional systems. However, off-the-shelf SVM classifiers are too inefficient for this task. Therefore, we present a method that makes the system substantially faster. This approach can also be applied to other similar tasks such as chunking and part-of-speech tagging. We also present an SVM-based feature selection method and an efficient training method.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/1ba84863e2685c45c5c41953444d9383dc7aa13b

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In Artificial Intelligence

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2. Chuhan Wu
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7. Tao Qi
8. Yonatan Belinkov

Citations in the corpus

• FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning (2020, methodology): “Thus, statistical methods have also been widely adopted, ranging from SVM (Isozaki and Kazawa, 2002) to more recent neural methods (Wang et al., 2018; Jain, 2015).”
• SVMs Modeling for Highly Imbalanced Classification (2009, methodology): “…be used to indicate the detection ability of a classifier between precision and recall as a function of varying a decision threshold [16] In this correspondence, the perf code, which is available at http://kodiak.cs.cornell.edu/kddcup/software.html, is utilized to calculate all of the four metrics.”
• Spherical Random Features for Polynomial Kernels (2015, background): “Another type of kernel that is used widely in many application domains is the polynomial kernel [12, 13], defined by K(x,y) = (〈x,y〉+ q)p, where q is the bias and p is the degree of the polynomial.”
• splitSVM: Fast, Space-Efficient, non-Heuristic, Polynomial Kernel Computation for NLP Applications (2008, methodology): “Thus, only pairs of common features appear in the resulting weight vector using the same expansion as in (Kudo and Matsumoto, 2003; Isozaki and Kazawa, 2002).”
• FedNER: Medical Named Entity Recognition with Federated Learning (2020, methodology): “Thus, statistical methods have also been widely adopted, ranging from SVM (Isozaki and Kazawa, 2002) to more recent neural methods (Wang et al., 2018; Jain, 2015).”
• Adaptive Co-attention Network for Named Entity Recognition in Tweets (2018, methodology): “Support vector machines have also been used on the NER task (Isozaki and Kazawa 2002).”
• A Survey on Deep Learning for Named Entity Recognition (2018, methodology): “Isozaki and Kazawa [79] developed a method to make SVM classifier substantially faster on NER task.”
• A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques (2017, methodology): “Most of the named entity recognition techniques are statistical learning methods such as hidden Markov models [13], maximum entropy models [29], support vector machines [69] and”