Neural network is a mathematical model to imitate the structure and function of the human nerves, which is one of the most rapidly developing research directions in the field of machine learning. Some significant breakthroughs have been achieved due to neural network technologies in a series of artificial intelligence tasks (such as image recognition, speech recognition and so on). Furthermore, neural network pro-vides a new perspective and method for the research of natural language processing. In this dissertation, I study the tasks in natural language understanding based on neural network technologies. The main contents are as follows. 1. Computing Chinese word similarity based on Crossed Recursive Deep Model This dissertation introduces a novel Crossed Recursive Deep Model (CRDM) for measuring Chinese word similarity. CRDM uses two neural networks, respec-tively, to quantify Chinese characters and words, then Chinese words similarity is computed by the word embeddings. The proposed approach has two properties. 1) the model skips the process of word segmentation, so as to eliminate the accumula-tion errors caused by Chinese word segmentation; 2) the model no longer uses large-scale dictionary or corpus, thereby reducing the effect of artificial rules. For task 4 of SemEval-2012(Chinese word similarity computing), the experiment results show that CRDM can achieve the best results compared with four systems submitted to SemEval-2012 and direct method of calculating similarity using word vectors. Thus CRDM provides a new perspective to compute Chinese word similarity. 2. Learning word embeddings by neural networks with statistics window model This dissertation designs a neural network with statistical window model for learning word embeddings, which can embed effectively the corpus statistics into the neural network. In the model, word-word co-occurrence information is introduced by statistical function of corpus, the distance information between two words is intro-duced by window function. Compared with general tool Word2Vector for word sim-ilarity task by data-sets (WordSim 353, RG, MC) and multiple dimensions(20, 50, 100), the experiment results show the validity of our model, which can learn word embeddings efficiently that better capture the semantics of words. 3. Sentiment analysis based on recursive neural network This dissertation introduces a hierarchical recursive neural network for sentiment analysis. A sentence is decomposed into differe...
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