Toward Conditional Models of Identity Uncertainty
with Application to Proper Noun Coreference
A. McCallum and B. Wellner
This paper is interesting. They make the point that pairwise decisions may not always be independent of others. One may be able to resolve inconsistencies by using a dependence model. They mention work, Relational Probabilistic Model, which captures this dependence. However since it is a generative model, they state this could lead to complexities due to many features with varying degrees of granularity. They discuss Hidden Markov models and conditional random fields briefly and Relational Markov networks as a similar model but improved classification.
They then discuss their work specifically which is "three conditional undirected graphical
models for identity uncertainty" which make the coreference decisions. Their first model connects mentions, entity-assignments, and each attribute of the mention. Edges indicate dependence. There is the concept of a clique, parameters may be part of different cliques which results in patterns of parameters called clique templates. Parts of the graph that depend on a number of entities are removed and replaced with random variables indicating coreference (Read this paper again to make sure we are clear on this). Per-entity attribute nodes are removed and replaced with attributes of mention. They then use graph partitioning. There is a lot in this paper and really requires another read to understand their methods better.
Saturday, August 20, 2011
Paper Summary - Disambiguation and Filter Methods in Using Web Knowledge for Coreference Resolution
Disambiguation and Filter Methods in Using Web Knowledge for Coreference Resolution
O. Uryupina and M. Poesio
They describe how they use Wikipedia and Yago to increase their Coreference Resolution performance. They use BART to support their efforts. Their classification consists of a anaphor and a potential antecedent, as they describe. Using their associated feature vectors, they use a 'maximum entropy classifier' to determine coreference. They used Wikipedia to improve their aliasing algorithm, which would perform string matching functions. They use Wikipedia based information as a feature, and to disambiguate mentions. They use Yago to supplement their efforts when there are too few features to make any reasonable decisions related to coreference. Yago information is also incorporated as a feature. They tested using ACE with reasonable scores.
Using these publicly available knowledge bases appears to improve performance (2-3% in this case). Something to think about....
O. Uryupina and M. Poesio
They describe how they use Wikipedia and Yago to increase their Coreference Resolution performance. They use BART to support their efforts. Their classification consists of a anaphor and a potential antecedent, as they describe. Using their associated feature vectors, they use a 'maximum entropy classifier' to determine coreference. They used Wikipedia to improve their aliasing algorithm, which would perform string matching functions. They use Wikipedia based information as a feature, and to disambiguate mentions. They use Yago to supplement their efforts when there are too few features to make any reasonable decisions related to coreference. Yago information is also incorporated as a feature. They tested using ACE with reasonable scores.
Using these publicly available knowledge bases appears to improve performance (2-3% in this case). Something to think about....
Monday, August 8, 2011
Stanford Online AI Course
This course is offered for Fall 2011 semester and the instructors are Sebastian Thrun and Peter Norvig. It should be a good class. The formal title is "Introduction to Artificial Intelligence".
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