Summary of Wikicausal: Corpus and Evaluation Framework For Causal Knowledge Graph Construction, by Oktie Hassanzadeh
WikiCausal: Corpus and Evaluation Framework for Causal Knowledge Graph Construction
by Oktie Hassanzadeh
First submitted to arxiv on: 31 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper presents a novel approach to constructing general-domain and domain-specific causal knowledge graphs. The authors propose a corpus, task, and evaluation framework for extracting causal relations between event concepts from Wikipedia articles. This enables reasoning for causal analysis and event prediction, with applications across different domains. The evaluation is performed using existing causal relations in Wikidata to measure recall, as well as Large Language Models to avoid manual or crowd-sourced evaluation. The authors demonstrate the effectiveness of their pipeline by relying on neural models for question answering and concept linking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to build big networks that understand cause-and-effect relationships. It creates a special collection of Wikipedia articles about different events, and challenges machine learning models to extract causal connections between these events. The authors want to see how well these models do, so they use two ways to check: one is by comparing the model’s answers to what we already know about causal relationships, and another is by using super-powerful language models that can understand a lot of text. The results show that their approach works well for finding the right model for each task. |
Keywords
» Artificial intelligence » Machine learning » Question answering » Recall
