[Paper Review] Fine-Grained Named Entity Typing over Distantly Supervised Data via Refinement in Hyperbolic Space.

Muhammad Ali, Yifang Sun+2 more|arXiv (Cornell University)|Jan 27, 2021

Topic Modeling25 references4 citations

TL;DR

This paper proposes FGNET-HR, a hyperbolic graph-based framework that leverages the hierarchical structure of fine-grained entity types by refining context-aware mention representations in hyperbolic space, achieving up to 3.5% improvement in strict accuracy over existing methods on distant supervision data with label noise.

ABSTRACT

Fine-Grained Named Entity Typing (FG-NET) aims at classifying the entity mentions into a wide range of entity types (usually hundreds) depending upon the context. While distant supervision is the most common way to acquire supervised training data, it brings in label noise, as it assigns type labels to the entity mentions irrespective of mentions' context. In attempts to deal with the label noise, leading research on the FG-NET assumes that the fine-grained entity typing data possesses a euclidean nature, which restraints the ability of the existing models in combating the label noise. Given the fact that the fine-grained type hierarchy exhibits a hierarchal structure, it makes hyperbolic space a natural choice to model the FG-NET data. In this research, we propose FGNET-HR, a novel framework that benefits from the hyperbolic geometry in combination with the graph structures to perform entity typing in a performance-enhanced fashion. FGNET-HR initially uses LSTM networks to encode the mention in relation with its context, later it forms a graph to distill/refine the mention's encodings in the hyperbolic space. Finally, the refined mention encoding is used for entity typing. Experimentation using different benchmark datasets shows that FGNET-HR improves the performance on FG-NET by up to 3.5% in terms of strict accuracy.

Motivation & Objective

• To address the challenge of label noise in distantly supervised fine-grained named entity typing (FG-NET) data.
• To model the hierarchical nature of fine-grained entity types more effectively than Euclidean space allows.
• To improve entity typing performance by leveraging the intrinsic geometry of hyperbolic space for representation refinement.
• To integrate graph-based message passing with hyperbolic geometry to refine context-aware mention encodings.
• To demonstrate that hyperbolic space better captures the hierarchical structure of entity types than traditional Euclidean approaches.

Proposed method

• Utilizes bidirectional LSTM networks to encode the context of each entity mention into dense vector representations.
• Constructs a graph structure where nodes represent entity mentions and edges represent contextual or semantic relationships.
• Performs graph-based message passing to refine the mention representations in hyperbolic space using hyperbolic geometry.
• Employs hyperbolic space to model the hierarchical structure of fine-grained entity types more naturally than Euclidean space.
• Applies hyperbolic projection and optimization techniques to preserve the hierarchical relationships during representation learning.
• Uses the refined hyperbolic embeddings as final representations for classification into fine-grained entity types.

Experimental results

Research questions

• RQ1 Can modeling the hierarchical structure of fine-grained entity types in hyperbolic space improve performance on distantly supervised FG-NET?
• RQ2 How does the integration of graph-based refinement in hyperbolic space compare to Euclidean-based methods in handling label noise?
• RQ3 To what extent does hyperbolic geometry enhance the representation of hierarchical entity types in context-aware encoding?
• RQ4 Does the proposed refinement mechanism in hyperbolic space lead to measurable gains in strict accuracy on benchmark datasets?

Key findings

• FGNET-HR achieves up to 3.5% absolute improvement in strict accuracy over strong baselines on multiple benchmark datasets.
• The use of hyperbolic space leads to better modeling of the hierarchical structure of fine-grained entity types compared to Euclidean space.
• Graph-based refinement in hyperbolic space effectively reduces the impact of label noise from distant supervision.
• The model demonstrates consistent performance gains across different datasets, indicating robustness to data distribution shifts.
• The integration of LSTM encoding with hyperbolic graph refinement yields superior representation learning for entity typing.
• The results validate that hyperbolic geometry is a more suitable inductive bias for hierarchical entity typing than Euclidean geometry.