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Bayesian Learning in Text Summarization
The paper presents a Bayesian model for text summarization, which explicitly en- codes and exploits information on how hu- man judgments are distributed over the text. Comparison is made against …...
An Incremental Bayesian Model for Learning Syntactic Categories
We present an incremental Bayesian model for the unsupervised learning of syntactic cate- gories from raw text. The model draws infor- mation from the distributional cues of words within an …...
Unsupervised Models for Coreference Resolution
We present a generative model for unsuper- vised coreference resolution that views coref- erence as an EM clustering process. For comparison purposes, we revisit Haghighi and Klein’s (2007) fully-generative Bayesian …...
Bayesian Extraction of Minimal SCFG Rules for Hierarchical Phrase-based Translation
We present a novel approach for extracting a minimal synchronous context-free grammar (SCFG) for Hiero-style statistical machine translation using a non-parametric Bayesian framework. Our approach is designed to ex- tract …...
Semi-Supervised Semantic Role Labeling: Approaching from an Unsupervised Perspective
Reducing the reliance of semantic role labeling (SRL) methods on human-annotated data has become an active area of research. However, the prior work has largely focused on either (1) looking …...
Clustering tweets usingWikipedia concepts
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Incremental Bayesian Learning of Semantic Categories
Models of category learning have been ex- tensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli. In this paper we focus on categories acquired from …...
A Bayesian model for joint word alignment and part-of-speech transfer
AbstractCurrent methods for word alignment require considerable amounts of parallel text to deliver accurate results, a requirement which is met only for a small minority of the world’s approximately 7,000 …...
Embedding Words as Distributions with a Bayesian Skip-gram Model
AbstractWe introduce a method for embedding words as probability densities in a low-dimensional space. Rather than assuming that a word embedding is fixed across the entire text collection, as in …...
Infinite SCAN: An Infinite Model of Diachronic Semantic Change
AbstractIn this study, we propose a Bayesian model that can jointly estimate the number of senses of words and their changes through time. The model combines a dynamic topic model …...