Parsing with CYK over Distributed Representations

Syntactic parsing is a key task in natural language processing. This task has been dominated by symbolic, grammar-based parsers. Neural networks, with their distributed representations, are challenging these methods. In this article we show that existing symbolic parsing algorithms can cross the border and be entirely formulated over distributed representations. To this end we introduce a version of the traditional Cocke-Younger-Kasami (CYK) algorithm, called D-CYK, which is entirely defined over distributed representations. Our D-CYK uses matrix multiplication on real number matrices of size independent of the length of the input string. These operations are compatible with traditional neural networks. Experiments show that our D-CYK approximates the original CYK algorithm. By showing that CYK can be entirely performed on distributed representations, we open the way to the definition of recurrent layers of CYK-informed neural networks.Comment: The algorithm has been greatly improved. Experiments have been redesigne

Predicting Embedded Syntactic Structures from Natural Language Sentences with Neural Network Approaches

Syntactic parsing is a key component of natural language understanding and, traditionally, has a symbolic output. Recently, a new approach for predicting syntactic structures from sentences has emerged: directly producing small and expressive vectors that embed in syntactic structures. In this approach, parsing produces distributed representations. In this paper, we advance the frontier of these novel predictors by using the learning capabilities of neural networks. We propose two approaches for predicting the embedded syntactic structures. The first approach is based on a multi-layer perceptron to learn how to map vectors representing sentences into embedded syntactic structures. The second approach exploits recurrent neural networks with long short-term memory (LSTM-RNN-DRP) to directly map sentences to these embedded structures. We show that both approaches successfully exploit word information to learn syntactic predictors and achieve a significant performance advantage over previous methods. Results on the Penn Treebank corpus are promising. With the LSTM-RNN-DRP, we improve the previous state-of-the-art method by 8.68%