We present the architecture of a deep learning pipeline for natural language processing. Based on this architecture we built a set of tools both for creating distributional vector representations and for performing specific NLP tasks. Three methods are available for creating embeddings: feedforward neural network, sentiment specific embeddings and embeddings based on counts and Hellinger PCA. Two methods are provided for training a network to perform sequence tagging, a window approach and a convolutional approach. The window approach is used for implementing a POS tagger and a NER tagger, the convolutional network is used for Semantic Role Labeling. The library is implemented in Python with core numerical processing written in C++ using parallel linear algebra library for efficiency and scalability.
DeepNL: a Deep Learning NLP pipeline
ATTARDI, GIUSEPPE
2015-01-01
Abstract
We present the architecture of a deep learning pipeline for natural language processing. Based on this architecture we built a set of tools both for creating distributional vector representations and for performing specific NLP tasks. Three methods are available for creating embeddings: feedforward neural network, sentiment specific embeddings and embeddings based on counts and Hellinger PCA. Two methods are provided for training a network to perform sequence tagging, a window approach and a convolutional approach. The window approach is used for implementing a POS tagger and a NER tagger, the convolutional network is used for Semantic Role Labeling. The library is implemented in Python with core numerical processing written in C++ using parallel linear algebra library for efficiency and scalability.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
