Enhancing Open Information Extraction with Linguistic Features and Pretrained Language Models

Belangrijkste concepten

Leveraging linguistic features and pretrained language models significantly improves Open Information Extraction performance.

Samenvatting

Abstract: Introduces Open Information Extraction (OIE) and the use of linguistic features with pretrained language models.
Introduction: Contrasts traditional OIE methods with neural approaches, highlighting the need for gold training data.
Related Work: Discusses recent advancements in structured prediction tasks like OIE.
Method: Proposes Weighted Addition and Linearized Concatenation techniques to enhance word embeddings with linguistic features.
Datasets, Processing, and Evaluation: Evaluates performance on various datasets, including LSOIE-extracted, ClausIE-extracted, and TANL-format datasets.
Experiments: Demonstrates significant improvements in Precision, Recall, and F1 scores using the proposed techniques.
Analysis: Compares the performance of Weighted Addition and Linearized Concatenation across different datasets.
Conclusion: Highlights the importance of incorporating SemDP tags as a useful linguistic feature for OIE tasks.
Limitations and Future Directions: Identifies challenges such as dataset quality issues and suggests future research directions.

Statistieken

Our work can give any neural OIE architecture a boost from both PLMs and linguistic features in one go.
We show wide improvements of up to 24.9%, 27.3%, and 14.9% on Precision, Recall, and F1 scores respectively over the baseline.

Citaten

"Our work can give any neural OIE architecture the key performance boost from both PLMs and linguistic features in one go."
"We show wide improvements of up to 24.9%, 27.3%, and 14.9% on Precision, Recall, and F1 scores respectively over the baseline."

Belangrijkste Inzichten Gedestilleerd Uit

Leveraging Linguistically Enhanced Embeddings for Open Information Extraction

by Fauzan Faroo... om arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.13903.pdf

Diepere vragen

How can dataset quality issues be addressed to improve OIE model performance?

Addressing dataset quality issues is crucial for improving OIE model performance. One approach is to manually reannotate datasets by trained judges to ensure accurate and high-quality triple extractions. Standardizing datasets to contain triples instead of n-ary tuples can also help make the data more suitable for downstream tasks like knowledge base creation. Additionally, using transparent, syntax-based rule systems like ClausIE to create synthetic datasets can provide cleaner and more comprehensive extractions compared to noisy human-annotated datasets like LSOIE.

What are the implications of incorporating SemDP tags as a useful linguistic feature for other NLP tasks?

Incorporating Semantic Dependency Parse (SemDP) tags as a linguistic feature has significant implications for various NLP tasks beyond Open Information Extraction (OIE). SemDP tags reduce computing overheads while maintaining or even enhancing model performance. The use of SemDP tags in neural networks allows for scalable linguistic features that contribute positively to the overall task accuracy. This suggests that SemDP could be a crucial step in incorporating useful and efficient linguistic features across different NLP applications.

How can neural OIE systems benefit from rule-based systems like ClausIE?

Neural Open Information Extraction (OIE) systems can benefit from rule-based systems like ClausIE in several ways:

Data Quality Improvement: Rule-based systems often provide cleaner and more comprehensive extractions compared to human-annotated datasets, leading to better training data quality.
Transparent Syntax-Based Rules: Incorporating rules from systems like ClausIE helps neural models learn explicit syntactic structures present in sentences, improving extraction accuracy.
Implicit Fact Extraction: Rule-based approaches excel at extracting implicit facts which may not be captured effectively by neural models alone.
Linguistic Structure Learning: By leveraging rules from ClausIE, neural OIE models can learn how specific linguistic structures relate to information extraction tasks, enhancing their overall performance and generalization capabilities.

By combining the strengths of both rule-based and neural approaches, OIE systems can achieve higher accuracy levels while benefiting from the transparency and efficiency provided by established rule sets such as those used in ClausIE.