Towards AMR-BR: A SemBank for Brazilian Portuguese Language (L18-1)

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Challenge: Abstract Meaning Representation (AMR) is a recent and prominent meaning representation with good acceptance and several applications in the Natural Language Processing area.
Approach: They propose to build an AMR annotated corpus for Brazilian Portuguese using an alignment-based approach.
Outcome: The proposed corpus is based on the Little Prince book, which went into the public domain and explored some language-specific annotation issues.

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Challenge: Abstract Meaning Representation (AMR) parsers require alignment between nodes and words of the sentence.
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Challenge: Existing Propbank corpus converts sense labels to a format which is more compatible with AMR and more robust to sparsity.
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Challenge: Abstract Meaning Representation (AMR) is limited to capturing the semantics of individual sentences.
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Towards Turkish Abstract Meaning Representation (P19-2)

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Challenge: Abstract Meaning Representation (AMR) abstracts away from syntactic features such as word order and does not annotate every constituent in a sentence.
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Challenge: Abstract Meaning Representation (AMR) is a semantic representation language for natural language processing.
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Challenge: Existing corpus for automatic post-editing of English and Brazilian Portuguese is limited.
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A Corpus of German Abstract Meaning Representation (DeAMR) (2024.lrec-main)

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Challenge: Abstract Meaning Representations (AMRs) are semantic graphs that abstract away from surface syntax and capture the meaning of who does what to whom in a sentence.
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Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations (D19-63)

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Challenge: Abstract Meaning Representation and Brazilian Portuguese (BP) are selected as semantic representation and language, respectively.
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The brWaC Corpus: A New Open Resource for Brazilian Portuguese (L18-1)

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Challenge: a large corpus for Brazilian Portuguese is needed for NLP applications . the corpus is 2.7 billion tokens, and domain diversity is maximized .
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