Abstract | We present a novel generative model that directly models the heuristic labeling process of distant supervision. |
Conclusion | Our generative model directly models the labeling process of DS and predicts patterns that are wrongly labeled with a relation. |
Experiments | Experiment 1 aimed to evaluate the performance of our generative model itself, which predicts whether a pattern expresses a relation, given a labeled corpus created with the DS assumption. |
Experiments | In our method, we trained a classifier with a labeled corpus cleaned by Algorithm 1 using the negative pattern list predicted by the generative model . |
Experiments | While our generative model does not use unlabeled examples as negative ones in detecting wrong labels, classifier-based approaches including MultiR do, suffering from false negatives. |
Generative Model | We now describe our generative model , which predicts whether a pattern expresses relation 7“ or not via hidden variables. |
Introduction | 0 To make the pattern prediction, we propose a generative model that directly models the process of automatic labeling in DS. |
Introduction | 0 Our variational inference for our generative model lets us automatically calibrate parameters for each relation, which are sensitive to the performance (see Section 6). |
Related Work | In our approach, parameters are calibrated for each relation by maximizing the likelihood of our generative model . |
Wrong Label Reduction | In the first step, we introduce the novel generative model that directly models DS’s labeling process and make the prediction (see Section 5). |
Conclusion | Experimental results show our approach discovers precise relation clusters and outperforms a generative model approach and a clustering method which does not address sense disambiguation. |
Evaluations | The generative model approach with 300 topics achieves similar precision to the hierarchical clustering approach. |
Evaluations | With more topics, the precision increases, however, the recall of the generative model is much lower than those of other approaches. |
Evaluations | The generative model approach produces more coherent clusters when the number of relation topics increases. |
Experiments | We compare our approach against several baseline systems, including a generative model approach and variations of our own approach. |
Experiments | Rel-LDA: Generative models have been successfully applied to unsupervised relation extraction (Rink and Harabagiu, 2011; Yao et al., 2011). |
Introduction | We compare our approach with several baseline systems, including a generative model approach, a clustering method that does not disambiguate between senses, and our approach with different features. |
Our Approach | The two theme features are extracted from generative models , and each is a topic number. |
Related Work | There has been considerable interest in unsupervised relation discovery, including clustering approach, generative models and many other approaches. |
Related Work | Our approach employs generative models for path sense disambiguation, which achieves better performance than directly applying generative models to unsupervised relation discovery. |
Abstract | In this paper, we propose a generative model that jointly identifies user-proposed refinements in instruction reviews at multiple granularities, and aligns them to the appropriate steps in the original instructions. |
Conclusion and Future Work | In this paper, we developed unsupervised methods based on generative models for mining refinements to online instructions from reviews. |
Introduction | Motivated by this, we propose a generative model for solving these tasks jointly without labeled data. |
Models | To identify refinements without labeled data, we propose a generative model of reviews (or more generally documents) with latent variables. |
Models | Foulds and Smyth (2011) propose a generative model for MIL in which the generation of the bag label y is conditioned on the instance labels 2. |
Related Work | We propose a generative model that makes predictions at both the review and review segment level. |
Experiments | Table 2: Perplexity of several generative models on Section 0 of the WSJ. |
Experiments | Our model outperforms all other generative models , though the improvement over the 71- gram model is not statistically significant. |
Experiments | We would like to use our model to make grammaticality judgements, but as a generative model it can only provide us with probabilities. |
Abstract | Most previous approaches have involved generative modeling of the distribution of pronunciations, usually trained to maximize likelihood. |
Introduction | In other words, these approaches optimize generative models using discriminative criteria. |
Introduction | We propose a general, flexible discriminative approach to pronunciation modeling, rather than dis-criminatively optimizing a generative model . |
Introduction | For generative models , phonetic error rate of generated pronunciations (Venkataramani and Byme, 2001) and |
Introduction | SAGE (Eisenstein et al., 2011a), a recently proposed sparse additive generative model of language, addresses many of the drawbacks of LDA. |
Prediction Experiments | In the second experiment, in addition to the linear kernel SVM, we also compare our SME model to a state-of-the-art sparse generative model of text (Eisenstein et al., 2011a), and vary the size of input vocabulary W exponentially from 29 to the full size of our training vocabulary4. |
Prediction Experiments | In this experiment, we compare SME with a state-of-the-art sparse generative model : SAGE (Eisenstein et al., 2011a). |
Prediction Experiments | Unlike hierarchical Dirichlet processes (Teh et al., 2006), in parametric Bayesian generative models , the number of topics K is often set manually, and can influence the model’s accuracy significantly. |
Introduction | Some techniques that have been used are Markov Random Fields (Poon and Domingos, 2009) and Bayesian generative models (Titov and Klemen-tiev, 2011). |
Unsupervised relational pattern learning | Figure 2: Plate diagram of the generative model used. |
Unsupervised relational pattern learning | Generative model Once these collections are built, we use the generative model from Figure 2 to learn the probability that a dependency path is conveying some relation between the entities it connects. |
Abstract | This model alone improves on previous generative models by 77%. |
Introduction | Our model outperforms the generative models of previous work by 77%. |
Timestamp Classifiers | We thus begin with a bag-of-words approach, reproducing the generative model used by both de J ong (2005) and Kanhabua and Norvag (2008; 2009). |
Modeling Multiparty Discussions | These topics are part of a generative model posited to have produced a corpus. |
Modeling Multiparty Discussions | In this section, we develop SITS, a generative model of multiparty discourse that jointly discovers topics and speaker-specific topic shifts from an unannotated corpus (Figure la). |
Topic Segmentation as a Social Process | Topic segmentation approaches range from simple heuristic methods based on lexical similarity (Morris and Hirst, 1991; Hearst, 1997) to more intricate generative models and supervised methods (Georgescul et al., 2006; Purver et al., 2006; Gruber et al., 2007; Eisenstein and Barzilay, 2008), which have been shown to outperform the established heuristics. |