Machine Learning Methods in Natural Language Information Extraction

Information extraction has become a research hotspot. According to the division of ACE (Automatic Content Extraction) conference evaluation tasks, the main research focuses on four areas: named entity recognition, entity relationship extraction, anaphora resolution, and event detection. Among them, entity recognition and relationship extraction are the most important parts of these tasks. Machine learning is a branch of computer science. Its focus is on developing algorithms that can be used to solve problems in the fields of pattern recognition, classification, prediction, and data analysis. Machine learning has been applied in NLP for several years. The most common machine learning technology in natural language processing (NLP) is called supervised learning. This technology involves training models using labeled data, where tags refer to the attributes or features of the training dataset. The goal is to predict which class or category should be assigned to a new unlabeled text sample based on prior knowledge of existing text attributes. This article focuses on machine learning and conducts research in the fields of natural language processing and transmission. In natural language processing, we first explore a general technology for generating word vectors, that is, integrating word embedding. By integrating the existing word embedding vector set and semantic knowledge base, we can generate a higher quality word embedding vector set.