Papers with open information extraction
EVIDENCEMINER: Textual Evidence Discovery for Life Sciences (2020.acl-demos)
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Xuan Wang, Yingjun Guan, Weili Liu, Aabhas Chauhan, Enyi Jiang, Qi Li, David Liem, Dibakar Sigdel, John Caufield, Peipei Ping, Jiawei Han
| Challenge: | EVIDENCEMINER is a web-based system that allows users to query a natural language statement and retrieve textual evidence from a background corpora for life sciences. |
| Approach: | They propose a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences. |
| Outcome: | EVIDENCEMINER is a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences. |
mOKB6: A Multilingual Open Knowledge Base Completion Benchmark (2023.acl-short)
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| Challenge: | Open knowledge bases (Open KBCs) are constructed from triples of the form, which can be denoted as (s, r, o) by using open information extraction (Open IE) systems. |
| Approach: | They construct a dataset with facts from Wikipedia in six languages . they use open information extraction systems to extract triples from text . |
| Outcome: | The proposed dataset contains facts from Wikipedia in six languages including English . it improves existing Open KB construction pipeline by doing multilingual coreference resolution and keeping only entity-linked triples . |
DeepStruct: Pretraining of Language Models for Structure Prediction (2022.findings-acl)
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| Challenge: | Pretrained language models perform structural understanding tasks that focus on understanding one aspect of the text. |
| Approach: | They propose a method for improving the structural understanding abilities of language models by pretraining them to generate structures from the text on task-agnostic corpora. |
| Outcome: | The proposed model performs state-of-the-art on 21 of 28 datasets. |
Zero-Shot Information Extraction as a Unified Text-to-Triple Translation (2021.emnlp-main)
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| Challenge: | a number of information extraction tasks require task-specific training. |
| Approach: | They propose a text-to-triple translation framework for information extraction tasks . they propose enabling task-agnostic translation by leveraging latent knowledge of a pre-trained language model . |
| Outcome: | The proposed framework outperforms the existing methods on open information extraction tasks. |
Multiˆ2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT (2020.findings-emnlp)
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| Challenge: | Existing open IE systems were based on handcrafted features or fine-grained rules. |
| Approach: | They propose a multi-head argument extraction method using multi-lingual BERT . they use a query, key, and value setting inspired by the Multimodal Transformer . |
| Outcome: | The proposed method outperforms existing sequence-labeling systems on two benchmark datasets and on two languages without training data. |
Facts That Matter (D18-1)
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| Challenge: | Existing methods to discover facts from natural language text are based on relation extraction and open information extraction. |
| Approach: | They propose a task of generating a machine-readable representation of the most prominent information in a text document as a set of facts. |
| Outcome: | The proposed system outperforms baselines and text summarizers in a supervised evaluation of salience tasks. |
Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl (L18-1)
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| Challenge: | DepCC is the largest-to-date linguistically analyzed corpus in English . large corpora are essential for the modern data-driven approaches to natural language processing . |
| Approach: | They present a large-to-date linguistically analyzed corpus in English with 365 million documents . they build an index of all sentences and their linguistic meta-data enabling quick search across the corpus . |
| Outcome: | The proposed model outperforms state-of-the-art models on smaller corpora on the SimVerb3500 dataset. |
Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction (2024.emnlp-main)
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| Challenge: | Existing methods for knowledge graph creation (KGC) are limited in their ability to scale up to text common in many real-world applications. |
| Approach: | They propose a framework for knowledge graph creation from input text using a pre-defined schema and a trained component that retrieves schema elements relevant to the input text. |
| Outcome: | The proposed framework extract-define-canonicalize extracts high-quality triplets with a succinct self-generated schema without any parameter tuning and with significantly larger schemas compared to prior works. |
IELM: An Open Information Extraction Benchmark for Pre-Trained Language Models (2022.emnlp-main)
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| Challenge: | Recent studies show pre-trained LMs store linguistic and relational knowledge . pre-training LM models can answer "fill-in-the-blank" questions based on pre-defined relations . |
| Approach: | They propose an open information extraction benchmark for pre-trained language models . they turn pre-trained LMs into zero-shot OIE systems to examine open relational information . |
| Outcome: | The proposed benchmark outperforms state-of-the-art methods on factual OIE datasets without training sets. |
Systematic Comparison of Neural Architectures and Training Approaches for Open Information Extraction (2020.emnlp-main)
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| Challenge: | Open information extraction (OIE) is a method for extracting facts from text in structured format . alternative formulations allow for longer tuples, but most work focuses on binary predicates only. |
| Approach: | They propose to extract facts from natural language text and represent them as structured triples . they compare different neural network architectures and training approaches . |
| Outcome: | The proposed approach improves the currently best models on the OIE16 benchmark by 0.421 F1 score and 0.420 AUC-PR . |
MARIO-0.5B: A Multi-Agent Lightweight Model for Real-Time Open Information Extraction in Low-Resource Settings (2025.findings-emnlp)
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| Challenge: | Large language models have shown remarkable capabilities in open information extraction, but their resource requirements often restrict their deployment in resource-constrained industrial settings. |
| Approach: | They introduce an ultra-lightweight large language model trained on instruction-based samples in Chinese, English, Korean, and Russian. |
| Outcome: | The proposed model outperforms large-scale models with up to 70B parameters, reducing computational resources by 140x and delivering 11x faster response times. |