Case-Based Generation of Regulatory Documents and Their Semantic Relatedness

Korger, Andreas; Baumeister, Joachim

doi:10.1007/978-3-030-39442-4_9

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Rule-based methods lend themselves well to domain-specific RE tasks (Aljamel et al, 2015;Peng et al, 2016;Wang et al, 2022). Aside from being highly interpretable, such methods define a set of rules manually created by domain experts and capture syntactic patterns that are associated with different types of relations observed in a corpus (Ravikumar et al, 2017;Korger and Baumeister, 2021;Xu et al, 2022). Advancements in machine learning (ML) and, more recently, deep learning (DL), have led to state-of-the-art performance in RE.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid of Rule-based and Transformer-based Approaches for Relation Extraction in Biodiversity Literature

Gabud,

Lapitan,

Mariano

et al. 2023

Proceedings of the 2nd Workshop on Pattern-Based Approaches to NLP in the Age of Deep Learning

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Relation extraction (RE) is one of the tasks behind many relevant natural language processing (NLP) applications. Exploiting the information hidden in millions of scholarly articles by leveraging NLP, specifically RE, systems could benefit studies in specialized domains, e.g. biomedicine and biodiversity. Although deep learning (DL)-based methods have shown state-of-the-art performance in many NLP tasks including RE, DL for domain-specific RE systems has been hindered by the lack of expertlabeled datasets which are typically required to train such methods. In this paper, we take advantage of the zero-shot (i.e., not requiring any labeled data) capability of pattern-based methods for RE using a rule-based approach, combined with templates for natural language inference (NLI) transformer models. We present our hybrid method for RE that exploits the advantages of both methods, i.e., interpretability of rules and transferability of transformers. Evaluated on a corpus of biodiversity literature with annotated relations, our hybrid method demonstrated an improvement of up to 15 percentage points in recall and best performance over solely rule-based and transformer-based methods with F1-scores ranging from 89.61% to 96.75% for reproductive condition -temporal expression relations, and ranging from 85.39% to 89.90% for habitat -geographic location relations.

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Section: Introductionmentioning

confidence: 99%

A Hybrid of Rule-based and Transformer-based Approaches for Relation Extraction in Biodiversity Literature

Gabud,

Lapitan,

Mariano

et al. 2023

Proceedings of the 2nd Workshop on Pattern-Based Approaches to NLP in the Age of Deep Learning

View full text Add to dashboard Cite

show abstract

“…Rule-based methods lend themselves well to domain-specific RE tasks (Aljamel et al, 2015;Peng et al, 2016;. Aside from being highly interpretable, such methods define a set of rules manually created by domain experts and capture syntactic patterns that are associated with different types of relations observed in a corpus (Ravikumar et al, 2017;Korger and Baumeister, 2021;. Advancements in machine learning (ML) and, more recently, deep learning (DL), have led to state-of-the-art performance in RE.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning

2023

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In the past year, the natural language processing (NLP) field (and the world at large!) has been hit by the large language model (LLM) "tsunami." This happened for the right reasons: LLMs perform extremely well in a multitude of NLP tasks, often with minimal training and, perhaps for the first time, have made NLP technology extremely approachable to non-expert users. However, LLMs are not perfect: they are not really explainable, they are not pliable, i.e., they cannot be easily modified to correct any errors observed, and they are not efficient due to the overhead of decoding. In contrast, rule-based methods are more transparent to subject matter experts; they are amenable to having a human in the loop through intervention, manipulation and incorporation of domain knowledge; and further the resulting systems tend to be lightweight and fast. This workshop focuses on all aspects of rule-based approaches, including their application, representation, and interpretability, as well as their strengths and weaknesses relative to state-of-the-art machine learning approaches.Considering the large number of potential directions in this neuro-symbolic space, we emphasized inclusivity in our workshop. We

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Case-Based Sample Generation Using Multi-Armed Bandits

Korger

Baumeister

2023

Lecture Notes in Computer Science

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Case-Based Generation of Regulatory Documents and Their Semantic Relatedness

Cited by 5 publications

References 10 publications

A Hybrid of Rule-based and Transformer-based Approaches for Relation Extraction in Biodiversity Literature

A Hybrid of Rule-based and Transformer-based Approaches for Relation Extraction in Biodiversity Literature

Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning

Case-Based Sample Generation Using Multi-Armed Bandits

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