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相关概念视频

Phylogenetic Trees03:21

Phylogenetic Trees

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Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
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The Tree of Life - Bacteria, Archaea, Eukaryotes02:40

The Tree of Life - Bacteria, Archaea, Eukaryotes

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The “tree of life” describes the evolution of life and the evolutionary relationships between organisms. The root of the tree is the common ancestor to all life on Earth. All other species radiate from this point, much like the branches of a tree. The numerous tips of these branches on the tree of life represent every living, or extant, species. Extinct species, which are species that no longer exist, can be found towards the center of the tree. Currently, these organisms, both...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Taxonomy01:31

Taxonomy

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Taxonomy is the science of defining and naming groups of biological organisms based on shared characteristics. It uses a hierarchy of increasingly inclusive categories with Latin names. The smallest units of taxonomy, species and genus, are used to assign a formal, taxonomic name to each species in a system. This classification system, referred to as binomial nomenclature, was formalized by Carolus Linnaeus in the 18th century.
Hierarchy of Taxonomy
The hierarchy that Carolus Linnaeus first...
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Phylogeny01:23

Phylogeny

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Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.
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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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相关实验视频

Updated: Jun 5, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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从理解和生成预训练的语言模型构建中国分类树.

Jianyu Guo1, Jingnan Chen1, Li Ren1

  • 1University of Electronic Science and Technology of China, ChengDu, Sichuan, China.

PeerJ. Computer science
|December 9, 2024
PubMed
概括

这项研究介绍了中国超关系推理模型 (CHRRM),用于构建中国超分类树. 它通过优化BERT模型和使用生成的大型语言模型来提高准确性,实现了15.67%的性能提升.

科学领域:

  • 自然语言处理自然语言处理.
  • 计算语言学 计算语言学
  • 知识表示 知识表示

背景情况:

  • 超的分类树对于理解NLP中的词汇关系至关重要.
  • 现有的建造这些树木的方法,特别是中国的方法,在准确性和效率方面面临挑战.
  • 预先训练的模型和生成的大型语言模型提供了改进的潜力.

研究的目的:

  • 开发和评估一种新的方法,CHRRM,用于构建中文语言领域的超高纳米分类树.
  • 通过使用预先训练的模型和生成的大型语言模型来提高超级关系提取的有效性.
  • 分析超参数优化和生成模型注释对分类结构准确性的影响.

主要方法:

  • 使用预先训练的模型 (例如,罗伯塔-wwm-ext-large) 进行初始的超关系预测.
  • 从预测的关系中形成一个最大跨度树来构建分类学.
  • 采用生成型的大型语言模型 (例如,ChatGPT,ChatGLM) 进行单词注释,以改善关系识别.
  • 为特定于分类结构的预训练模型优化超参数.

主要成果:

  • 在CHRRM模型中,在hypernym分类结构中实现了15.67%的显著相对改进.
  • 在中国WORDNET验证数据集上获得了67.9的F1得分,比之前的58.7.7有所增加.
关键词:
生成型的大型语言模型超的分类学树 超的分类学树预先训练有素的语言模型

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  • 罗伯塔-wwm-ext-large在分类树结构中表现出卓越的表现.
  • 生成型的大型语言模型在提高准确性方面表现有前途,但在质量和资源方面存在局限性.

    • 结论:

    • 该CHRRM模型,利用优化的预训练模型和生成的LLM,有效地构建了中国的超 taxonomy树.
    • 生成型的大型语言模型可以改善下游自然语言理解 (NLU) 任务,尽管需要考虑资源限制.
    • 该研究验证了将生成式LLM集成到NLP任务 (如分类结构) 的可行性和好处.