This work introduces a model free approach to sentence compression, which grew out of ideas from Nomoto (2008), and examines how it compares to a state-of-art model intensive appr...
Conventional sentence compression methods employ a syntactic parser to compress a sentence without changing its meaning. However, the reference compressions made by humans do not ...
Recent work on Conditional Random Fields (CRFs) has demonstrated the need for regularisation when applying these models to real-world NLP data sets. Conventional approaches to regu...
The paper presents a novel sentence trimmer in Japanese, which combines a non-statistical yet generic tree generation model and Conditional Random Fields (CRFs), to address improv...
— Large Clusters, high availability clusters and Grid deployments often suffer from network, node or operating system faults and thus require the use of fault tolerant programmin...