EAPSI: Generating Word Embeddings using Extreme Learning Machines for Classifying Clinical Texts

EAPSI：使用极限学习机生成词嵌入来对临床文本进行分类

• 批准号: 1614024
• 负责人: Paula Lauren
• 金额: $ 0.54万
• 依托单位: Lauren Paula
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-15 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1614024&HistoricalAwards=false
• 关键词: EAPSI; Generating; Word; Embeddings; using

In 1950, the computer scientist Alan Turing proposed a test for true artificial intelligence. In Turing's view, a computer must be considered intelligent if it could understand human language. After more than 60 years of research, this is still an ongoing effort. Recent methods, including neural language models that use advanced statistics, have made great strides towards realizing Turing's vision. This study builds on existing research to explore methods for improving computer-based natural language understanding. The research will be conducted under the mentorship of Professor Guang-bin Huang, a noted expert on machine learning, of Nanyang Technological University.Natural language processing (NLP) involves the development of computer-based algorithms to understand natural language. Statistical language models are typically used for various NLP tasks, including machine translation and text categorization. Language models based on neural networks, also known as neural embeddings, map words (or phrases) to a numerical representation in a low-dimensional space. Typically, neural networks use back-propagation for training a neural network, which results in slow training. Extreme Learning Machines (ELM) is a type of neural network, where hidden neurons are randomly generated hidden nodes. This study involves the use of ELM for faster training in the generation of neural embeddings.This award under the East Asia and Pacific Summer Institutes program supports summer research by a U.S. graduate student and is jointly funded by NSF and the National Research Foundation of Singapore.

1950年，计算机科学家艾伦·图灵（Alan Turing）提出了一个测试真正人工智能的方法。在图灵看来，如果一台计算机能理解人类的语言，它就必须被认为是智能的。 经过60多年的研究，这仍然是一个持续的努力。最近的方法，包括使用高级统计的神经语言模型，在实现图灵的愿景方面取得了巨大进展。 这项研究建立在现有的研究基础上，探索提高基于计算机的自然语言理解的方法。 这项研究将在南洋理工大学著名机器学习专家黄光斌教授的指导下进行。自然语言处理（NLP）涉及开发基于计算机的算法来理解自然语言。统计语言模型通常用于各种NLP任务，包括机器翻译和文本分类。 基于神经网络的语言模型，也称为神经嵌入，将单词（或短语）映射到低维空间中的数值表示。通常，神经网络使用反向传播来训练神经网络，这导致训练缓慢。极限学习机（ELM）是一种神经网络，其中隐藏的神经元是随机生成的隐藏节点。这项研究涉及使用ELM来更快地训练神经嵌入的生成。该奖项属于东亚和太平洋夏季研究所计划，支持美国研究生的夏季研究，由NSF和新加坡国家研究基金会共同资助。