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Related Experiment Video

Updated: Jul 12, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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DAT-MT Accelerated Graph Fusion Dependency Parsing Model for Small Samples in Professional Fields.

Rui Li1, Shili Shu1, Shunli Wang1

  • 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China.

Entropy (Basel, Switzerland)
|October 28, 2023
PubMed
Summary

This study introduces a novel dependency parsing model that accelerates text analysis by combining specialized and general language data. The new method significantly improves accuracy and parsing speed for massive texts.

Keywords:
dependency parsingdouble-array triegraph fusionmulti-threadingsmall samplespecific professional fields

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Area of Science:

  • Computational Linguistics
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Information overload in massive texts necessitates efficient processing methods.
  • Domain-specific syntactic analysis requires effective corpus migration and faster models.
  • Current dependency parsing models face challenges with space and time complexity.

Purpose of the Study:

  • To develop a domain-adaptive dependency parsing model for improved accuracy and speed.
  • To address the computational complexity of graph fusion models in syntactic analysis.
  • To enhance the efficiency of syntactic analysis for massive texts in specialized domains.

Main Methods:

  • Proposed a Double-Array Trie and Multi-threading (DAT-MT) accelerated graph fusion dependency parsing model.
  • Combined specialized syntactic features from professional corpora with generalized features from news corpora.
  • Implemented rapid mapping of Chinese character features and parallel processing for enhanced speed.

Main Results:

  • Achieved significant improvements in unlabeled attachment score (UAS) and labeled attachment score (LAS) compared to single-corpus models.
  • Outperformed existing deep learning-based methods like DDParser and LTP 4 in accuracy.
  • Demonstrated a 3.7x speedup compared to a red-black tree index and single-thread approach.

Conclusions:

  • The DAT-MT model offers an efficient and accurate solution for dependency parsing, particularly for specialized domains.
  • This approach enhances real-time processing of massive texts, benefiting applications like semantic correlation and knowledge graph construction.
  • The model's effectiveness in combining diverse linguistic features and optimizing computational performance marks a significant advancement in syntactic analysis.