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The Importance of Syntactic Parsing and Inference in Semantic Role Labeling
Syntactic Parsing Inference Semantic Role Labeling
2015/9/6
We present a general framework for semantic role labeling. The framework combines a machine-learning technique with an integer linear programming-based inference procedure, which incorporates linguist...
Sometimes Average is Best:The Importance of Averaging for Prediction using MCMC Inference in Topic Modeling
Sometimes Average is Best Averaging for Prediction MCMC Inference Topic Modeling
2015/9/2
Markov chain Monte Carlo (MCMC) approximates the posterior distribution of latent variable models bygenerating many samples and averaging over them. In practice, however, itis often more convenient to...
Representation and Inference for Natural Language: A First Course in Computational Semantics
Natural Language Computational Semantics
2015/9/1
Computational semanticsis the study of how to represent meaning in a way that computers can use. For the authors of this textbook, this study includes the representation of
the meaning of natural lan...
Connectionist models and Bayesian inference.
Fast Exact Inference with a Factored Model for Natural Language Parsing
Fast Exact Inference Factored Model Natural Language Parsing
2015/6/12
We present a novel generative model for natural language tree structures in which semantic (lexical dependency) and syntactic (PCFG) structures are scored with separate models. This factorization prov...
Verb Sense and Subcategorization:Using Joint Inference to Improve Performance on Complementary Tasks
Verb Sense and Subcategorization Performance Complementary Tasks
2015/6/12
We propose a general model for joint inference in correlated natural language processing tasks when fully annotated training data is not available, and apply this model to the dual tasks of word sense...
Robust Textual Inference using Diverse Knowledge Sources
Textual Inference Diverse Knowledge
2015/6/12
We present a machine learning approach to robust textual inference, in which parses of the text and the hypothesis sentences are used to measure their asymmetric “similarity”, and thereby to decide if...
Solving the Problem of Cascading Errors:Approximate Bayesian Inference for Linguistic Annotation Pipelines
Problem of Cascading Errors Approximate Bayesian Inference Linguistic Annotation Pipelines
2015/6/12
The end-to-end performance of natural language processing systems for compound tasks, such as question answering and textual entailment, is often hampered by use of a greedy 1-best pipeline architectu...
A Phrase-Based Alignment Model for Natural Language Inference
Phrase-Based Alignment Model Natural Language
2015/6/12
The alignment problem—establishing links between corresponding phrases in two related sentences—is as important in natural language inference (NLI) as it is in machine translation (MT). But the tools ...