Introduction | This is achieved by constructing a generative model that includes phrases at many levels of granularity, from minimal phrases all the way up to full sentences. |
Phrase Extraction | As has been noted in previous works, (Koehn et al., 2003; DeNero et al., 2006) exhaustive phrase extraction tends to outperform approaches that use syntax or generative models to limit phrase boundaries. |
Phrase Extraction | (2006) state that this is because generative models choose only a single phrase segmentation, and thus throw away many good phrase pairs that are in conflict with this segmentation. |
Related Work | While they take a supervised approach based on discriminative methods, we present a fully unsupervised generative model . |
Experimental Results | Two representative methods were used as baselines: the generative model proposed by (Brill and Moore, 2000) referred to as generative and the logistic regression model proposed by (Okazaki et al., 2008) |
Experimental Results | Usually a discriminative model works better than a generative model , and that seems to be what happens with small k’s. |
Introduction | In spelling error correction, Brill and Moore (2000) proposed employing a generative model for candidate generation and a hierarchy of trie structures for fast candidate retrieval. |
Related Work | For example, Brill and Moore (2000) developed a generative model including contextual substitution rules; and Toutanova and Moore (2002) further improved the model by adding pronunciation factors into the model. |
Enriched Two-Tiered Topic Model | Once the level and conditional path is drawn (see level generation for ETTM above) the rest of the generative model is same as TTM. |
Introduction | In this paper, we introduce a series of new generative models for multiple-documents, based on a discovery of hierarchical topics and their correlations to extract topically coherent sentences. |
Multi-Document Summarization Models | we utilize the advantages of previous topic models and build an unsupervised generative model that can associate each word in each document with three random variables: a sentence S, a higher-level topic H, and a lower-level topic T, in an analogical way to PAM models (Li and McCallum, 2006), i.e., a directed acyclic graph (DAG) representing mixtures of hierarchical structure, where super-topics are multi-nomials over subtopics at lower levels in the DAG. |