Jooho-Lee
School of Computer Science and
Information Engineering, The Catholic
University of Korea
E-mail: jooho414@gmail.com
2023-03-31

1
 Introduction
• Problem statement
• Contributions
 Related Works
 Methodology

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Introduction
Problem Statement
• Lack of context in natural language processing: It is sometimes difficult to effectively grasp the sentence
structure and context of the text.
• Data sparsity problem in natural language processing: Text data has the problem of sparsity as the length
increases.
• Polynominal problems in natural language processing: Sometimes the same words or phrases have different
meanings, making it difficult for natural language processing models to accurately grasp the context.
• Unstructured data problems in natural language processing: Text data is unstructured, so it is difficult to process.

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Introduction
Contribution
• Gain Language Integration: AMR is not dependent on specific languages, and provides a foundation for
integrating and processing different languages.
• Conservation of Semantic Connectivity: AMR converts sentences into semantic structures while preserving
semantic connectivity between words. This allows you to understand the meaning of a sentence more accurately.

4
Related Works
Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text Classification
Conference on Neural Information Processing Systems 2021

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Related Works

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Methodology
How to construct AMR graph?
• Nodes: AMR graphs consist of a set of nodes. Each node represents a concept or entity, and has a word or
phrase describing that concept or object.
• Edge: Relationships between nodes are represented by edges. An edge connects two nodes and represents a
semantic relationship between them. For example, :arg0 edge represents the relationship between the node's
subject and verb (verb).
• Root: The AMR graph has one root node, all of which are associated with the root node.
• Value: A node can have a value. Values represent information such as string, number, or time.
• Repeated Concepts: Repeated concepts have the same meaning and are used when repeated. In this case,
add :same-of-edge between repeated concepts to indicate that it is the same concept.
• Negative Concepts: Negative concepts are displayed using the :polarity edge.
• Date and Time: The date and time are expressed in ISO 8601 format.

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Methodology
How to construct AMR graph?
1. Tokenizing
2. Allocate concept node to each word token
3. Add entity node
4. Make the connectivity between nodes

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Methodology
How to construct AMR graph?
“John has a dog.”
1. ['John', 'has', 'a', 'dog', '.’]
2. John -> [n1] has -> [v1] a -> [d1] dog -> [n2] . -> [p1]
3. [n1 : person, name "John"] [v1 : have-rel] [d1 : op] [n2 : animal, name "dog"] [p1 : op]