This paper studies the use of discrete-time recurrent neural networks for predicting the next symbol in a sequence. The focus is on online prediction, a task much harder than the c...
We present a very efficient, in terms of space and access speed, data structure for storing huge natural language data sets. The structure is described as LZ (Ziv Lempel) compresse...
We present a supervised machine learning algorithm for metonymy resolution, which exploits the similarity between examples of conventional metonymy. We show that syntactic head-mo...
We have designed and implemented a text processing system that can extract important information from hundreds of paragraphs per hour and can be transported within weeks to a new ...
Real-world natural language sentences are long and complex, and always contain unexpected grammatical constructions. It even includes noise and ungrammaticality. This paper descri...