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相关概念视频

Functional Brain Systems: Limbic System01:15

Functional Brain Systems: Limbic System

The limbic system, often called the "emotional brain," is a complex set of structures located deep within the brain. The intricate network of the limbic system supports a wide range of psychological functions, from emotional regulation to memory formation and sensory processing. This functional brain region encompasses specific parts of the diencephalon and the cerebrum, integrating the higher mental functions of the cerebral cortex with the primitive emotional responses of the deep brain...
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or playing an...
Chunking and Rehearsal in Sensory Memory01:22

Chunking and Rehearsal in Sensory Memory

Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of information more...
Storage01:23

Storage

A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze each...
Long-Term Memory01:18

Long-Term Memory

Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
Long-term memory can be categorized into two primary types: explicit and implicit memory. Explicit memory, also known as declarative memory, involves the conscious recollection of information that we deliberately try to remember, recall, and articulate. This type of memory encompasses specific facts, events, and...
Chunking01:12

Chunking

Chunking is a powerful cognitive technique that improves short-term memory retention by organizing information into smaller, more manageable units. The brain, limited by working memory capacity, can more easily process and store information when it is divided into "chunks" rather than presented as discrete, unrelated elements. Chunking is especially useful when dealing with large amounts of information, such as numerical sequences, words, or complex ideas.
The principle behind chunking is...

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泰语单词分割与大脑启发的稀疏分布式表示学习记忆学习记忆.

Thasayu Soisoonthorn1, Herwig Unger2, Maleerat Maliyaem1

  • 1King Mongkut's University of Technology North Bangkok, Faculty of Information Technology and Digital Innovation, Bangkok, Thailand.

Computational intelligence and neuroscience
|June 7, 2023
PubMed
概括

两种由大脑启发的新方法使用稀疏分布式表示 (SDR) 增强了泰国词汇细分. 这些方法比现有方法显著提高了准确性和性能,其中一种方法在学习词汇上获得了近乎完美的分数.

科学领域:

  • 自然语言处理自然语言处理.
  • 计算神经科学是一种神经科学.
  • 人工智能的人工智能

背景情况:

  • 泰语由于其非细分的性质,对单词细分提出了独特的挑战.
  • 错误的单词细分严重影响下游自然语言处理 (NLP) 任务的执行.
  • 现有的细分方法经常在准确性和上下文依赖的词识别方面扎.

研究的目的:

  • 为准确的泰国单词细分引入两种新的大脑启发的计算方法.
  • 利用受新皮层结构启发的稀疏分布式表示 (SDR) 来提高NLP中的信息处理.
  • 评估与已建立和最先进的泰国单词细分技术相比拟的方法.

主要方法:

  • 开发THDICTSDR:一种基于字典的方法,增强了SDR用于上下文学习和n-gram集成.
  • 开发THSDR:一种使用SDR进行单词细分的无字典方法.
  • 使用BEST2010和LST20数据集对最长匹配,NewMM和Deepcut (最先进的深度学习) 的比较评估.

主要成果:

  • 与传统的基于字典的方法相比,THDICTSDR显著提高了准确性和性能.
  • THDICTSDR获得了95.60%的F1-Score,与Deepcut的96.34%相比,并且在学习词汇上以96.78%的成绩表现出色.

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  • 在所有测试的情况下,THSDR显示出对噪声的容错性,并且在所有测试情况下都超过了深度学习方法,在完全学习的句子上获得了99.48%的F1-Score.
  • 结论:

    • 提出的大脑启发的方法,特别是THDICTSDR,在泰国单词细分准确性和效率方面取得了实质性的进步.
    • SDR提供了一个强大的机制,用于上下文理解和强大的性能在NLP任务,模仿神经处理.
    • 无论是THDICTSDR还是THSDR,都为当前最先进的细分方法提供了可行和有效的替代方案,其中THSDR提供了独特的噪音处理能力.