Learning incremental syntactic structures with recursive neural networks

We develop novel algorithmic ideas for building a natural language parser grounded upon the hypothesis of incrementality, which is widely supported by experimental data as a model of human parsing. Our proposal relies on a machine learning technique for predicting the correctness of partial syntactic structures that are built during the parsing process. A recursive neural network architecture is employed for computing predictions after a training phase on examples drawn from a corpus of parsed sentences, the Penn Treebank. Our results indicate the viability of the approach and lay out the premises for a novel generation of algorithms for natural language processing which more closely model human parsing. These algorithms may prove very useful in the development of efficient parsers and have an immediate application in the construction of semiautomatic annotation tools