Summary of Span-oriented Information Extraction — a Unifying Perspective on Information Extraction, by Yifan Ding et al.
Span-Oriented Information Extraction – A Unifying Perspective on Information Extraction
by Yifan Ding, Michael Yankoski, Tim Weninger
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework for natural language processing (NLP) is introduced, which unifies various information extraction tasks by framing them in terms of “spans” within text. This approach redefines common NLP tasks, such as linking unstructured text to structured data, and presents a unified perspective on the information extraction process. The framework leverages the concept of spans to organize diverse information extraction techniques, making it easier to develop and compare models across different tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Information Extraction is a set of natural language processing (NLP) tasks that help us find important parts in text and what they mean. For many years, these tasks have been used to extract relevant info and connect free text to structured data. The problem is that these tasks are very different from each other, which makes it hard to make progress in this area. To solve this issue, a new way of looking at information extraction is proposed, focusing on “spans” within text. This approach groups many NLP tasks together under one umbrella, making it easier for researchers and developers to work with these tasks and create better models. |
Keywords
» Artificial intelligence » Natural language processing » Nlp
