Span-Oriented Information Extraction -- A Unifying Perspective on Information Extraction

Yifan Ding, Michael Yankoski, Tim Weninger
3/18/2024
cs.CLcs.AIcs.IR

No confident code implementation found for this paper.

Abstract

Information Extraction refers to a collection of tasks within Natural Language Processing (NLP) that identifies sub-sequences within text and their labels. These tasks have been used for many years to link extract relevant information and to link free text to structured data. However, the heterogeneity among information extraction tasks impedes progress in this area. We therefore offer a unifying perspective centered on what we define to be spans in text. We then re-orient these seemingly incongruous tasks into this unified perspective and then re-present the wide assortment of information extraction tasks as variants of the same basic Span-Oriented Information Extraction task.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

A General Framework for Information Extraction using Dynamic Span Graphs

83% match1 repo1862019

Entity, Relation, and Event Extraction with Contextualized Span Representations

80% match1 repo5932019

Mirror: A Universal Framework for Various Information Extraction Tasks

79% match1 repo1222023

An Empirical Study on Information Extraction using Large Language Models

79% match2024

An Empirical Study on Information Extraction using Large Language Models

79% match1 repo1332023

SpanRE: Entities and Overlapping Relations Extraction Based on Spans and Entity Attention

78% match2023

Cite this paper

@article{ding2024spanoriented,
  title  = {Span-Oriented Information Extraction -- A Unifying Perspective on Information Extraction},
  author = {Yifan Ding and Michael Yankoski and Tim Weninger},
  year   = {2024},
  eprint = {2403.15453},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.15453v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2403.15453.svg)](https://codeofpaper.com/paper/2403.15453)

Discussion