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Article
Open AccessA survey on large language model based autonomous agents
Autonomous agents have long been a research focus in academic and industry communities. Previous research often focuses on training agents with limited knowledge within isolated environments, which diverges si...
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Chapter and Conference Paper
Exploring the Effectiveness of Student Behavior in Prerequisite Relation Discovery for Concepts
What knowledge should a student grasp before beginning a new MOOC course? To answer this question, it is essential to automatically discover prerequisite relations among course concepts. Although researchers h...
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Chapter
Representation Learning for Compositional Semantics
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Chapter
Knowledge Representation Learning and Knowledge-Guided NLP
Knowledge is an important characteristic of human intelligence and reflects the complexity of human languages. To this end, many efforts have been devoted to organizing various human knowledge to improve the a...
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Chapter
Legal Knowledge Representation Learning
The law guarantees the regular functioning of the nation and society. In recent years, legal artificial intelligence (legal AI), which aims to apply artificial intelligence techniques to perform legal tasks, h...
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Chapter
OpenBMB: Big Model Systems for Large-Scale Representation Learning
Big pre-trained models (PTMs) have received increasing attention in recent years from academia and industry for their excellent performance on downstream tasks. However, huge computing power and sophisticated ...
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Chapter
Word Representation Learning
Words are the building blocks of phrases, sentences, and documents. Word representation is thus critical for natural language processing (NLP). In this chapter, we introduce the approaches for word representat...
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Chapter
Sentence and Document Representation Learning
Sentence and document are high-level linguistic units of natural languages. Representation learning of sentences and documents remains a core and challenging task because many important applications of natural...
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Chapter
Graph Representation Learning
Graph structure, which can represent objects and their relationships, is ubiquitous in big data including natural languages. Besides original text as a sequence of word tokens, massive additional information i...
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Chapter
Robust Representation Learning
Representation learning models, especially pre-trained models, help NLP systems achieve superior performances on multiple standard benchmarks. However, real-world environments are complicated and volatile, whi...
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Chapter
Sememe-Based Lexical Knowledge Representation Learning
Linguistic and commonsense knowledge bases describe knowledge in formal and structural languages. Such knowledge can be easily leveraged in modern natural language processing systems. In this chapter, we intro...
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Chapter
Biomedical Knowledge Representation Learning
As a subject closely related to our life and understanding of the world, biomedicine keeps drawing much attention from researchers in recent years. To help improve the efficiency of people and accelerate the p...
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Chapter
Ten Key Problems of Pre-trained Models: An Outlook of Representation Learning
The aforementioned representation learning methods have shown their effectiveness in various NLP scenarios and tasks. Large-scale pre-trained language models (i.e., big models) are the state of the art of repr...
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Book
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Chapter
Pre-trained Models for Representation Learning
Pre-training-fine-tuning has recently become a new paradigm in natural language processing, learning better representations of words, sentences, and documents in a self-supervised manner. Pre-trained models no...
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Chapter
Cross-Modal Representation Learning
Cross-modal representation learning is an essential part of representation learning, which aims to learn semantic representations for different modalities including text, audio, image and video, etc., and thei...
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Chapter
Secure Networked Control Systems Design Using Semi-homomorphic Encryption
A secure and private nonlinear networked control systems (NCSs) design using semi-homomorphic encryption is studied. Static feedback controllers are used and network architectures are provided to enable cont...
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Chapter
RETRACTED CHAPTER: Document Representation
The authors have retracted this Chapter because significant portions of the text are duplicated from [1] and [2]. The authors apologise to readers for this error. All authors agree with this retraction.
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Chapter
Correction to: Representation Learning for Natural Language Processing
In the original version of the book, the following belated correction has been incorporated in both Preface and Chapter 8. In the Preface, a new reference citation has been added in Chapter 8.
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Chapter
Outlook
The aforementioned representation learning models and methods have shown their effectiveness in various NLP scenarios and tasks. With the rapid growth of data scales and the development of computation devices,...