A Survey on Neural Open Information Extraction: Current Status and Future Directions

• URL: http://arxiv.org/abs/2205.11725v1
• Date: Tue, 24 May 2022 02:24:55 GMT
• Title: A Survey on Neural Open Information Extraction: Current Status and Future Directions
• Authors: Shaowen Zhou, Bowen Yu, Aixin Sun, Cheng Long, Jingyang Li, Jian Sun
• Abstract summary: Open Information Extraction (OpenIE) facilitates domain-independent discovery of relational facts from large corpora. We provide an overview of the-state-of-the-art neural OpenIE models, their key design decisions, strengths and weakness.
• Score: 87.30702606041407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open Information Extraction (OpenIE) facilitates domain-independent discovery of relational facts from large corpora. The technique well suits many open-world natural language understanding scenarios, such as automatic knowledge base construction, open-domain question answering, and explicit reasoning. Thanks to the rapid development in deep learning technologies, numerous neural OpenIE architectures have been proposed and achieve considerable performance improvement. In this survey, we provide an extensive overview of the-state-of-the-art neural OpenIE models, their key design decisions, strengths and weakness. Then, we discuss limitations of current solutions and the open issues in OpenIE problem itself. Finally we list recent trends that could help expand its scope and applicability, setting up promising directions for future research in OpenIE. To our best knowledge, this paper is the first review on this specific topic.

Related papers

• Towards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence [18.130525337375985]
  This paper presents a framework for grappling with openness across the AI stack. It summarizes previous work on this topic, analyzes the various potential reasons to pursue openness. It outlines how openness varies in different parts of the AI stack, both at the model and at the system level.
  arXiv  Detail & Related papers  (2024-05-17T20:35:39Z)
• Open-world Machine Learning: A Review and New Outlooks [83.6401132743407]
  This paper aims to provide a comprehensive introduction to the emerging open-world machine learning paradigm. It aims to help researchers build more powerful AI systems in their respective fields, and to promote the development of artificial general intelligence.
  arXiv  Detail & Related papers  (2024-03-04T06:25:26Z)
• PIVOINE: Instruction Tuning for Open-world Information Extraction [53.98073623222221]
  We consider the problem of Open-world Information Extraction (Open-world IE), which extracts comprehensive entity profiles from unstructured texts. We develop a large language model (LLM) that is able to perform Open-world IE to extract desirable entity profiles characterized by (possibly fine-grained) natural language instructions. In particular, we construct INSTRUCTOPENWIKI, a substantial instruction tuning dataset for Open-world IE enriched with a comprehensive corpus, extensive annotations, and diverse instructions.
  arXiv  Detail & Related papers  (2023-05-24T08:52:08Z)
• Towards Generalized Open Information Extraction [74.20080376460947]
  We propose a more realistic scenario: generalizing over unseen target domains with different data distributions from the source training domains. DragonIE beats the previous methods in both in-domain and out-of-domain settings by as much as 6.4% in F1 score absolutely.
  arXiv  Detail & Related papers  (2022-11-29T07:33:44Z)
• A Survey on Open Information Extraction from Rule-based Model to Large Language Model [29.017823043117144]
  Open Information Extraction (OpenIE) represents a crucial NLP task aimed at deriving structured information from unstructured text. This survey paper provides an overview of OpenIE technologies spanning from 2007 to 2024, emphasizing a chronological perspective. The paper categorizes OpenIE approaches into rule-based, neural, and pre-trained large language models, discussing each within a chronological framework.
  arXiv  Detail & Related papers  (2022-08-18T08:03:45Z)
• Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering [62.88322725956294]
  We review the latest research trends in OpenQA, with particular attention to systems that incorporate neural MRC techniques. We introduce modern OpenQA architecture named Retriever-Reader'' and analyze the various systems that follow this architecture. We then discuss key challenges to developing OpenQA systems and offer an analysis of benchmarks that are commonly used.
  arXiv  Detail & Related papers  (2021-01-04T04:47:46Z)
• State-of-the-art Techniques in Deep Edge Intelligence [0.0]
  Edge Intelligence (EI) has quickly emerged as a powerful alternative to enable learning using the concepts of Edge Computing. In this article, we provide an overview of the major constraints in operationalizing DEI.
  arXiv  Detail & Related papers  (2020-08-03T12:17:23Z)
• Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research Dataset: Preliminary Thoughts and Lessons Learned [88.42878484408469]
  We present the Neural Covidex, a search engine that exploits the latest neural ranking architectures. This paper describes our initial efforts and offers a few thoughts about lessons we have learned along the way.
  arXiv  Detail & Related papers  (2020-04-10T17:12:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.