An Improved LSTM Structure for Natural Language Processing

Natural language processing technology is widely used in artificial intelligence fields such as machine translation, human-computer interaction and speech recognition. Natural language processing is a daunting task due to the variability, ambiguity and context-dependent interpretation of human language. The current deep learning technology has made great progress in NLP technology. However, many NLP systems still have practical problems, such as high training complexity, computational difficulties in large-scale content scenarios, high retrieval complexity and lack of probabilistic significance. This paper proposes an improved NLP method based on long short-term memory (LSTM) structure, whose parameters are randomly discarded when they are passed backwards in the recursive projection layer. Compared with baseline and other LSTM, the improved method has better F1 score results on the Wall Street Journal dataset, including the word2vec word vector and the one-hot word vector, which indicates that our method is more suitable for NLP in limited computing resources and high amount of data.