Semi-Supervised Sequence Modeling with Syntactic Topic Models

By Wei Li, Andrew McCallum. 2005

70 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Semi-supervised sequence tagging with bidirectional language models (2017, methodology): “The main components in TagLM, our language-model-augmented sequence tagging system.”
Word Representations: A Simple and General Method for Semi-Supervised Learning (2010, methodology): “Li and McCallum (2005) use an HMM-LDA model to improve POS tagging and Chinese Word Segmentation.”
Semi-Supervised Structured Output Learning Based on a Hybrid Generative and Discriminative Approach (2007, background): “In fact, many attempts have recently been made to develop semi-supervised SOL methods (Zhu et al., 2003; Li and McCallum, 2005; Altun et al., 2005; Jiao et al., 2006; Brefeld and Scheffer, 2006).”
Semi-Supervised Learning for Natural Language (2005, methodology): “…models using mutual information features; CRF (closed and open) refers to (Peng et al., 2004) using closed and open feature sets; and CRF+HMMLDA refers to (Li and McCallum, 2005). . . . . . . . . . 67 5.8 Dev and Test F1 scores obtained by the M+F and SM+F models on named-entity recognition.”
Simple Semi-supervised Dependency Parsing (2008, methodology): “, Li and McCallum (2005)).”
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data (2010, methodology): “In this paper we compare against a representative graph-based label propagationmethod called Quadratic Cost Criterion (QC) (Bengio et al., 2006) whose results are reported in Chapelle et al. (2006).”
Semi-Supervised Sequential Labeling and Segmentation Using Giga-Word Scale Unlabeled Data (2008, methodology): “There is an approach that combines individually and independently trained joint PMs into a discriminative model (Li and McCallum, 2005).”
Feature Noising for Log-Linear Structured Prediction (2013, background): “NLP sequence labeling tasks are especially well suited to a semi-supervised approach, as input features are numerous but sparse, and labeled data is expensive to obtain but unlabeled data is abundant (Li and McCallum, 2005; Jiao et al., 2006).”
Simple, robust, scalable semi-supervised learning via expectation regularization (2007, methodology): “Instead of using data clustering directly to position the decision boundary, other methods pre-cluster unlabeled data, and use these clusters as features for supervised training on the labeled data (Miller et al., 2004; Li & McCallum, 2005).”