The advantage of LSA is that it is efficient in representing world knowledge without the need for manual coding of relations and that it has in fact been considered to simulate aspects of human knowledge representation. An overview of LSA applications will be given, followed by some further ...
Pre-trained language representation models, such as BERT, capture a general language representation from large-scale corpora, but lack domain-specific knowledge. When reading a domain text, experts make inferences with relevant knowledge. For machines to achieve this capability, we propose a knowledge...
Continuous Representation:使用一个嵌入向量来表示实体。具体来说,给定事实文本“Bert is a character on [MASK]”,这种方式使用[MASK]对应的最后一层的hidden states来表示模型提取出的知识表示,然后用这个嵌入表示在一个训练好的实体向量库中寻找最接近的实体作为预测。结果表示这种表示方式可以在存储足够多实体的同时...
Velardi, Representation and control strategies for large knowledge domains: an application to NLP, Applied Artificial Intelligence, v.2 n.3-4, p.213-249, 1988 [doi>10.1080/08839518808949909]Representation and Control Strategies for large Knowledge Domains: An Application to NLP - Antonacci, Russo,...
Language models as knowledge base, locating knowledge in large language models Lifelong learning, unlearning and etc. Security and privacy for large language models Comparisons of different technologies 📜 Resources This is a collection of research and review papers of Knowledge Editing. Any suggestions...
alleviate the heterogeneity issue. We propose a novel structure-enhanced self-attention mechanism to encode the relational information and keep the globally semantic information among sub-graphs. Moreover, we propose masked knowledge modeling as a new paradigm for knowledge graph representation learning. ...
The “graph” in Knowledge Graph refers to a way of organizing data that highlights relationships between data points.Graphrepresentation looks like a network of interconnected points. This is in contrast to databases like Oracle or MySQL —relationalsystems — where data is stored in tables. Relati...
为训练该模型,本文采用cpu-gpu混合训练策略结合负采样机制减少训练时间;最终本文提出的方法在知识图谱补全和若干NLP任务上均带来了增益。 5、Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning 论文链接:https://arxiv.org/pdf/2004.14224.pdf ...
NLP University 开张大吉--李维老师,Simon Fraser University,博士 ACL 2019 知识图谱的全方位总结 NLP 相关论文解析 The Illustrated Transformer An Attentive Survey of Attention Models BERT:Bidirectional Encoder Representations from Transformers ERNIE:Enhanced Representation through Knowledge Integration ...
语义依赖图上的节点可以通过semantic role labeling (SRL)和dependency parsing抽取得到,然后节点可以通过不同的关系进行连接。Jin等人提出一个语义依赖指导的摘要模型,堆叠多个encoder模块,这些encoder包括一个序列encoder和一个图encoder。其他工作也借助abstract meaning representation(AMR)作为语义图。