A Survey of Event Causality Identification: Principles, Taxonomy, Challenges, and Assessment
- URL: http://arxiv.org/abs/2411.10371v2
- Date: Mon, 25 Nov 2024 16:55:09 GMT
- Title: A Survey of Event Causality Identification: Principles, Taxonomy, Challenges, and Assessment
- Authors: Qing Cheng, Zefan Zeng, Xingchen Hu, Yuehang Si, Zhong Liu,
- Abstract summary: Event Causality Identification (ECI) has become a crucial task in Natural Language Processing (NLP)
Our taxonomy classifies ECI methods according to the two primary tasks of sentence-level (SECI) and document-level (DECI) event causality identification.
- Score: 6.492836595169771
- License:
- Abstract: Event Causality Identification (ECI) has become a crucial task in Natural Language Processing (NLP), aimed at automatically extracting causalities from textual data. In this survey, we systematically address the foundational principles, technical frameworks, and challenges of ECI, offering a comprehensive taxonomy to categorize and clarify current research methodologies, as well as a quantitative assessment of existing models. We first establish a conceptual framework for ECI, outlining key definitions, problem formulations, and evaluation standards. Our taxonomy classifies ECI methods according to the two primary tasks of sentence-level (SECI) and document-level (DECI) event causality identification. For SECI, we examine feature pattern-based matching, deep semantic encoding, causal knowledge pre-training and prompt-based fine-tuning, and external knowledge enhancement methods. For DECI, we highlight approaches focused on event graph reasoning and prompt-based techniques to address the complexity of cross-sentence causal inference. Additionally, we analyze the strengths, limitations, and open challenges of each approach. We further conduct an extensive quantitative evaluation of various ECI methods on two benchmark datasets. Finally, we explore future research directions, highlighting promising pathways to overcome current limitations and broaden ECI applications.
Related papers
- A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation [0.0]
Advancements in image segmentation play an integral role within the greater scope of Deep Learning-based computer vision.
Uncertainty quantification has been extensively studied within this context, enabling expression of model ignorance (epistemic uncertainty) or data ambiguity (aleatoric uncertainty) to prevent uninformed decision making.
This work provides a comprehensive overview of probabilistic segmentation by discussing fundamental concepts in uncertainty that govern advancements in the field and the application to various tasks.
arXiv Detail & Related papers (2024-11-25T13:26:09Z) - A Comprehensive Survey on Evidential Deep Learning and Its Applications [64.83473301188138]
Evidential Deep Learning (EDL) provides reliable uncertainty estimation with minimal additional computation in a single forward pass.
We first delve into the theoretical foundation of EDL, the subjective logic theory, and discuss its distinctions from other uncertainty estimation frameworks.
We elaborate on its extensive applications across various machine learning paradigms and downstream tasks.
arXiv Detail & Related papers (2024-09-07T05:55:06Z) - On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey [82.49623756124357]
Zero-shot image recognition (ZSIR) aims at empowering models to recognize and reason in unseen domains.
This paper presents a broad review of recent advances in element-wise ZSIR.
We first attempt to integrate the three basic ZSIR tasks of object recognition, compositional recognition, and foundation model-based open-world recognition into a unified element-wise perspective.
arXiv Detail & Related papers (2024-08-09T05:49:21Z) - Coding for Intelligence from the Perspective of Category [66.14012258680992]
Coding targets compressing and reconstructing data, and intelligence.
Recent trends demonstrate the potential homogeneity of these two fields.
We propose a novel problem of Coding for Intelligence from the category theory view.
arXiv Detail & Related papers (2024-07-01T07:05:44Z) - MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs [55.20845457594977]
Large language models (LLMs) have shown increasing capability in problem-solving and decision-making.
We present a process-based benchmark MR-Ben that demands a meta-reasoning skill.
Our meta-reasoning paradigm is especially suited for system-2 slow thinking.
arXiv Detail & Related papers (2024-06-20T03:50:23Z) - A Logically Consistent Chain-of-Thought Approach for Stance Detection [4.895189262775054]
Zero-shot stance detection (ZSSD) aims to detect stances toward unseen targets.
We introduce a novel approach named Logically Consistent Chain-of-Thought (LC-CoT) for ZSSD.
LC-CoT improves stance detection by ensuring relevant and logically sound knowledge extraction.
arXiv Detail & Related papers (2023-12-26T13:54:00Z) - SSL Framework for Causal Inconsistency between Structures and
Representations [23.035761299444953]
Cross-pollination of deep learning and causal discovery has catalyzed a burgeoning field of research seeking to elucidate causal relationships within non-statistical data forms like images, videos, and text.
We theoretically develop intervention strategies suitable for indefinite data and derive causal consistency condition (CCC)
CCC could potentially play an influential role in various fields.
arXiv Detail & Related papers (2023-10-28T08:29:49Z) - Few-shot Class-incremental Learning: A Survey [16.729567512584822]
Few-shot Class-Incremental Learning (FSCIL) presents a unique challenge in Machine Learning (ML)
This paper aims to provide a comprehensive and systematic review of FSCIL.
arXiv Detail & Related papers (2023-08-13T13:01:21Z) - A Study of Situational Reasoning for Traffic Understanding [63.45021731775964]
We devise three novel text-based tasks for situational reasoning in the traffic domain.
We adopt four knowledge-enhanced methods that have shown generalization capability across language reasoning tasks in prior work.
We provide in-depth analyses of model performance on data partitions and examine model predictions categorically.
arXiv Detail & Related papers (2023-06-05T01:01:12Z) - Review of coreference resolution in English and Persian [8.604145658574689]
Coreference resolution (CR) identifies expressions referring to the same real-world entity.
This paper explores the latest advancements in CR, spanning coreference and anaphora resolution.
Recognizing the unique challenges of Persian CR, we dedicate a focused analysis to this under-resourced language.
arXiv Detail & Related papers (2022-11-08T18:14:09Z) - Uncertainty Quantification for Deep Context-Aware Mobile Activity
Recognition and Unknown Context Discovery [85.36948722680822]
We develop a context-aware mixture of deep models termed the alpha-beta network.
We improve accuracy and F score by 10% by identifying high-level contexts.
In order to ensure training stability, we have used a clustering-based pre-training in both public and in-house datasets.
arXiv Detail & Related papers (2020-03-03T19:35:34Z)
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.