Titlebook: Representation Learning for Natural Language Processing; Zhiyuan Liu,Yankai Lin,Maosong Sun Book‘‘‘‘‘‘‘‘ 2023Latest edition The Editor(s)

武器

BUST

Ten Key Problems of Pre-trained Models: An Outlook of Representation Learning, models (i.e., big models) are the state of the art of representation learning for NLP and beyond. With the rapid growth of data scale and the development of computation devices, big models bring us to a new era of AI and NLP. Standing on the new giants of big models, there are many new challenges a

我不死扛

Sentence and Document Representation Learning,ument representation learning. Finally, we present representative applications of sentence and document representation, including text classification, sequence labeling, reading comprehension, question answering, information retrieval, and sequence-to-sequence generation.

ARCH

Graph Representation Learning,ter, we introduce a variety of graph representation learning methods that embed graph data into vectors with shallow or deep neural models. After that, we introduce how graph representation learning helps NLP tasks.

比喻好

Knowledge Representation Learning and Knowledge-Guided NLP,knowledge, including knowledge representation learning, knowledge-guided NLP, and knowledge acquisition. For linguistic knowledge, commonsense knowledge, and domain knowledge, we will introduce them in detail in subsequent chapters considering their unique knowledge properties.

神秘

uveitis

Legal Knowledge Representation Learning,gal AI. In this chapter, we summarize the existing knowledge-intensive legal AI approaches regarding knowledge representation, acquisition, and application. Besides, future directions and ethical considerations are also discussed to promote the development of legal AI.

MUTE

Aids209

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