Abstract
As one of the core tasks in the field of natural language processing, syntactic analysis has always been a hot topic for researchers, including tasks such as Questions and Answer (Q&A), Search String Comprehension, Semantic Analysis, and Knowledge Base Construction. This paper aims to study the application of deep learning and neural network in natural language syntax analysis, which has significant research and application value. This paper first studies a transfer-based dependent syntax analyzer using a feed-forward neural network as a classifier. By analyzing the model, we have made meticulous parameters of the model to improve its performance. This paper proposes a dependent syntactic analysis model based on a long-term memory neural network. This model is based on the feed-forward neural network model described above and will be used as a feature extractor. After the feature extractor is pretrained, we use a long short-term memory neural network as a classifier of the transfer action, and the characteristics extracted by the syntactic analyzer as its input to train a recursive neural network classifier optimized by sentences. The classifier can not only classify the current pattern feature but also multirich information such as analysis of state history. Therefore, the model is modeled in the analysis process of the entire sentence in syntactic analysis, replacing the method of modeling independent analysis. The experimental results show that the model has achieved greater performance improvement than baseline methods.
Highlights
As one of the core tasks in the field of natural language processing, syntactic analysis has always been a hot topic for researchers, including tasks such as Questions and Answer (Q&A), Search String Comprehension, Semantic Analysis, and Knowledge Base Construction. is paper aims to study the application of deep learning and neural network in natural language syntax analysis, which has significant research and application value. is paper first studies a transfer-based dependent syntax analyzer using a feed-forward neural network as a classifier
We have made meticulous parameters of the model to improve its performance. is paper proposes a dependent syntactic analysis model based on a long-term memory neural network. is model is based on the feed-forward neural network model described above and will be used as a feature extractor
After the feature extractor is pretrained, we use a long short-term memory neural network as a classifier of the transfer action, and the characteristics extracted by the syntactic analyzer as its input to train a recursive neural network classifier optimized by sentences. e classifier can classify the current pattern feature and multirich information such as analysis of state history. erefore, the model is modeled in the analysis process of the entire sentence in syntactic analysis, replacing the method of modeling independent analysis. e experimental results show that the model has achieved greater performance improvement than baseline methods
Summary
As one of the core tasks in the field of natural language processing, syntactic analysis has always been a hot topic for researchers, including tasks such as Questions and Answer (Q&A), Search String Comprehension, Semantic Analysis, and Knowledge Base Construction. is paper aims to study the application of deep learning and neural network in natural language syntax analysis, which has significant research and application value. is paper first studies a transfer-based dependent syntax analyzer using a feed-forward neural network as a classifier. Introduction e study of grammar in computational linguistics refers to the study of specific structures and rules contained in language, such as finding the rules of the order of words in sentences and classifying words [1] Linear laws in these languages can be expressed using methods such as Language Model and Part-of-Speech Tagging. For the nonlinear information in the sentence, we can use Syntactic Structure or Dependency Relation between words in the sentence to express This analysis and expression of sentence structure may not be the ultimate goal of natural language processing problems, it is often an important step to solve the problem [2], which is used in such as search query understanding [3], Question Answering, QA [4] and Semantic Parsing and other issues have important applications. Deep learning learns from large-scale data to intricate structural representations
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