Symbolic Representation:通过扩充语言模型的词表,将所有实体都表示为语言模型词表中的词。这种表示方法让模型最为准确的将实体存储下来,但是由于模型对事实知识进行完形填空时,需要计算出每个词的概率然后进行softmax,这种低效率的知识提取方法导致模型无法容纳足够多的实体。 Surface Form Representation:用维基数据中提供的...
事实上,现在 NLP Community 的关注点已经逐渐从诸如 NMT 的几个点的 BLEU 值转向让 NLP 系统更加智能的方向。这是一个好事,也许未来不久的某一天,我们就能吃上机器人做的菜了! Reference Homepage of Yejin Choi The Missing Representation in Neural (Language) Models-Yejin Chor VERB PHYSICS: Relative Physi...
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...
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,...
In the development of Word Manager, these problems have been taken into account. As a result, its knowledge acquisition component is well-developed, and its knowledge representation enables more flexible use than typical finite-state systems.
Natural Language Processing and Knowledge Representation: Language for Knowledge and Knowledge for Language. Cambridge: MIT Press. 2000. ISBN 0-262-59021-2 pb, Price $41.46, xviii + 459 pages We compare the word sense disambiguation systems submitted for the English-all-words task in SENSEVAL-2...
ERNIE应用于自然语言推理、语义相似度、命名实体识别、情感分析和问答等5项中文NLP任务。 4.3.1 Natural Language Inference 跨语言自然语言推理Cross-lingual Natural Language Inference(XNLI)语料库(Liu等人,2019年)是多NLI语料库的众包集合。这两对被注释为文本蕴涵,并被翻译成包括中文在内的14种语言。标签包含矛盾...
分类: Representation Learning , Recommender Systems 标签: empirical , NLP , novel , seminal , RAG , retrieval 馒头and花卷 粉丝- 93 关注- 1 会员号:2578(终身会员VIP) +加关注 0 0 « 上一篇: Query2box Reasoning over Knowledge Graphs in Vector Space using Box Embeddings » 下一篇:...
The approach described here allows using membership function to represent imprecise and uncertain knowledge by learning in Fuzzy Semantic Networks. This representation has a great practical interest due to the possibility to realize on the one hand, the construction of this membership function from a ...
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...