MLRIP: Pre-training a military language representation model with informative factual knowledge and professional knowledge base

TL;DR

Comprehensive evaluations on military-speciffc NLP tasks show that MLRIP outperforms existing BERT-based models by substantial margins, establishing new state-of-the-art performance in military entity recognition, typing, and operational linkage extraction tasks while demonstrating superior operational efffciency in resource-constrained environments.

Abstract

Incorporating structured knowledge into pre-trained language models has demonstrated signiffcant bene-ffts for domain-speciffc natural language processing tasks, particularly in specialized ffelds like military intelligence analysis. Existing approaches typically integrate external knowledge through masking tech-niques or fusion mechanisms, but often fail to fully leverage the intrinsic tactical associations and factual information within input sequences, while introducing uncontrolled noise from unveriffed exter-nal sources. To address these limitations, we present MLRIP (Military Language Representation with Integrated Prior), a novel pre-training framework that introduces a hierarchical knowledge integration pipeline combined with a dual-phase entity substitu-tion mechanism. Our approach speciffcally models operational linkages between military entities, capturing critical dependencies such as command, support, and engagement structures. Comprehensive evaluations on military-speciffc NLP tasks show that MLRIP outperforms existing BERT-based models by substantial margins, establishing new state-of-the-art performance in military entity recognition, typing, and operational linkage extraction tasks while demonstrating superior operational efffciency in resource-constrained environments.

Figures (6)

• Figure 1: Comparative analysis of knowledge integration approaches between MLRIP and ERNIE-Baidu sun2019ernie. Both frameworks employ similar methodologies for basic lexical and phrasal masking stages. However, MLRIP introduces substantial innovations in entity-centric processing through contextual prediction mechanisms that utilize available entity information and operational linkages. In contrast to ERNIE-Baidu's exclusive use of MLM for entity reconstruction, MLRIP integrates factual knowledge from the sentence context to enhance masked entity prediction. Furthermore, MLRIP extends the masking paradigm to include operational linkage-level processing, specifically designed to capture military-specific relationships and tactical associations between entities. The framework additionally incorporates external domain knowledge through two novel mechanisms: semantic-preserving entity substitution and fact-based replacement strategies optimized for military text processing.
• Figure 2: Comprehensive architectural overview of the MLRIP framework, illustrating the integrated components for military text representation learning, including the enhanced embedding mechanism and progressive knowledge assimilation stages.
• Figure 3: Entity-centric processing methodology, demonstrating how military entities are identified and processed as complete units with contextual factual knowledge integration.
• Figure 4: Operational interrelationship modeling methodology, illustrating how military-specific relationships between entities are identified and processed.
• Figure 5: Entity substitution methodology, illustrating both semantic-preserving and fact-based replacement strategies for military entities.