Grunnleggende konsepter
A unifying perspective on information extraction tasks, centered around spans in text.
Sammendrag
Information Extraction refers to tasks within NLP that identify sub-sequences within text and their labels.
The article proposes a unified perspective on information extraction tasks based on spans in text.
Different transformations of spans are discussed, including sequential labeling, token prototyping, token-pair classification, span classification, span locating, and span generation.
Various models and approaches for each transformation type are explored.
Evaluation metrics for information extraction systems are also discussed.
Statistikk
"Information Extraction refers to a collection of tasks within Natural Language Processing (NLP) that identifies sub-sequences within text and their labels."
"These tokens are what is actually used to train NLP systems."
"Most natural language processing (NLP) systems do not take as input data in its native sequence-of-bytes format."
Sitater
"The rise of LLMs and prompt tuning also fit nicely into this paradigm because the emergent ability of LLMs could be further empowered if the generated tokens could consider the duality inherent in their spans."