Information Extraction
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Benchmarks
SemTabNet
Most implemented
Beyond Sentiment: Structured Information Extraction from Financial News
Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs
Papers
MomentQuant: an even more minimalist interval method with linear time complexity for time series classification
Time series data is very common in many real-world applications and in numerous domains, with increasing interest for automated information extraction using machine learning. One of these subfields is time series classif…
Time Series ClassificationInformation ExtractionAutomated Analysis Framework for Multilingual Climate-Health Literature Based on Multi-Agent Large Language Model
The rapid proliferation of interdisciplinary and multilingual scientific literature has left traditional manual analysis and single-algorithm methods plagued by low efficiency, poor scalability, and insufficient domain a…
Information ExtractionCorporateBench: Large-Scale Q&A Benchmarking with Temporal Knowledge Bases
LLMs are increasingly able to answer complex questions about enterprise-scale document collections. But evaluation is hard: companies don't want to share internal communications, and synthetic datasets have been overly s…
Information ExtractionMulti-Granularity Context-Enhanced RAG over Multimodal Knowledge Graphs
Retrieval-augmented generation (RAG) is widely used to mitigate hallucination issues in large language models (LLMs) and multimodal large language models (MLLMs). In particular, knowledge graph (KG)-based RAG leverages s…
Information ExtractionKnowledge GraphsWhen "Must" Becomes "Maybe": Constraint Weakening in LLM Agent Workflows
Large language model (LLM) agents coordinate complex tasks through multi-role and multi-stage workflows. Upstream state is repeatedly transformed into intermediate language artifacts, such as summaries, plans, tickets, m…
Information ExtractionA Scalable Cross-Domain Event Extraction System via a Unified Generative Training Framework
Event extraction is fundamental to information extraction. Prior approaches often separate event detection and argument extraction or depend on dataset-specific designs, limiting scalability and cross-domain generalizati…
Information ExtractionDomain GeneralizationEvent Extraction