A Bi-consolidating Model for Extracting Overlapping Relational Triples from Sentences

Core Concepts

A bi-consolidating model is proposed to simultaneously reinforce the local and global semantic features relevant to each relational triple, which is effective for extracting overlapping relational triples from sentences.

Abstract

The content discusses a bi-consolidating model for joint relational triple extraction. Key highlights:

The task of relational triple extraction suffers from a serious semantic overlapping problem, where several relation triples may share one or two entities in a sentence. This makes it challenging to learn discriminative semantic features relevant to a relation triple.

The proposed bi-consolidating model consists of two components:

Local consolidation component: Uses pixel difference convolution to enhance the semantic information of a possible triple representation from adjacent regions and mitigate noise in neighboring elements.
Global consolidation component: Strengthens the triple representation based on channel attention and spatial attention, which can learn remote semantic dependencies in a sentence.

The bi-consolidating model is designed to simultaneously reinforce the local and global semantic features relevant to each relation triple, which helps improve the performance of both entity identification and relation type classification.

Extensive experiments on four benchmark datasets show that the proposed model consistently outperforms existing state-of-the-art models and achieves competitive performance, especially on datasets with serious semantic overlapping problems.

Analytical experiments demonstrate the effectiveness of the bi-consolidating model and provide insights for other natural language processing tasks.

Stats

The sentence usually contains several overlapped entity pairs, which can be divided into four categories: normal, entity pair overlapping (EPO), single entity overlapping (SEO) and subject object overlapping (SOO).
The 2D sentence representation unfolds a semantic plane, where each element denotes an entity pair representation. Entity semantics are spread out in the 2D representation, which is influential on the extraction performance.

Quotes

"Current methods to extract relational triples directly make a prediction based on a possible entity pair in a raw sentence without depending on entity recognition. The task suffers from a serious semantic overlapping problem, in which several relation triples may share one or two entities in a sentence."
"To advance the discriminability of of deep networks, it is important to make full use of local features relevant to a named entity and to encode long-distance semantic dependences (or global features) relevant to a named entity pair."

Key Insights Distilled From

A Bi-consolidating Model for Joint Relational Triple Extraction

by Xiaocheng Lu... at arxiv.org 04-08-2024

https://arxiv.org/pdf/2404.03881.pdf

Deeper Inquiries

How can the proposed bi-consolidating model be extended to other natural language processing tasks beyond relational triple extraction

The proposed bi-consolidating model can be extended to other natural language processing tasks beyond relational triple extraction by adapting its architecture and components to suit the specific requirements of different tasks. For tasks like named entity recognition, sentiment analysis, or text classification, the local consolidation component can be modified to focus on extracting relevant features for entities or sentiment-bearing words. The global consolidation component can be adjusted to capture broader contextual information or dependencies across the entire text. By customizing these components and integrating them into the respective task pipelines, the bi-consolidating model can effectively enhance performance in a variety of NLP tasks.

What are the potential limitations of the bi-consolidating model, and how could they be addressed in future research

One potential limitation of the bi-consolidating model could be its scalability to larger datasets or more complex linguistic structures. As the model relies on a 2D sentence representation and pixel difference convolutions, it may face challenges in processing extremely long texts or texts with intricate syntactic and semantic patterns. To address this limitation, future research could explore techniques to optimize the computational efficiency of the model, such as incorporating hierarchical structures or attention mechanisms to handle longer sequences more effectively. Additionally, further experimentation and fine-tuning of hyperparameters could help improve the model's performance on diverse datasets and tasks.

How might the insights gained from the analytical experiments on the 2D sentence representation be applied to improve other sentence representation techniques for various NLP tasks

The insights gained from the analytical experiments on the 2D sentence representation can be applied to improve other sentence representation techniques for various NLP tasks by focusing on enhancing both local and global semantic features. For tasks requiring entity recognition or relation extraction, the concept of a 2D sentence representation can be leveraged to capture intricate relationships between entities and their contexts. By incorporating pixel difference convolutions and attention mechanisms, similar to the bi-consolidating model, other sentence representation techniques can effectively encode local and global semantic dependencies. This approach can lead to more robust and accurate representations for a wide range of NLP tasks, ultimately improving overall performance and generalization capabilities.