2-Phase Passage Re-ranking Model based on Neural-Symbolic Ranking Models

Bae, Yongjin; Hyun, Kim Do; Joon-Ho, Lim; Kim, Hyunki; 이공주,

doi:10.5626/jok.2021.48.5.501

Cited by 1 publication

(1 citation statement)

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“…Table 1 illustrates the degree to which analysis performance can be enhanced by replacing the optimal path with a lower‐ranked node. This problem is analogous to the challenge of reranking search results in information retrieval [34], where the goal is to identify the correct answer among the generated suboptimal results.…”

Section: Introductionmentioning

confidence: 99%

Transformer‐based reranking for improving Korean morphological analysis systems

Ryu,

Lim,

Kwon

et al. 2024

ETRI Journal

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This study introduces a new approach in Korean morphological analysis combining dictionary‐based techniques with Transformer‐based deep learning models. The key innovation is the use of a BERT‐based reranking system, significantly enhancing the accuracy of traditional morphological analysis. The method generates multiple suboptimal paths, then employs BERT models for reranking, leveraging their advanced language comprehension. Results show remarkable performance improvements, with the first‐stage reranking achieving over 20% improvement in error reduction rate compared with existing models. The second stage, using another BERT variant, further increases this improvement to over 30%. This indicates a significant leap in accuracy, validating the effectiveness of merging dictionary‐based analysis with contemporary deep learning. The study suggests future exploration in refined integrations of dictionary and deep learning methods as well as using probabilistic models for enhanced morphological analysis. This hybrid approach sets a new benchmark in the field and offers insights for similar challenges in language processing applications.

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Section: Introductionmentioning

confidence: 99%