Finding Needles in Emb(a)dding Haystacks: Legal Document Retrieval via Bagging and SVR Ensembles

• URL: http://arxiv.org/abs/2501.05018v1
• Date: Thu, 09 Jan 2025 07:21:44 GMT
• Title: Finding Needles in Emb(a)dding Haystacks: Legal Document Retrieval via Bagging and SVR Ensembles
• Authors: Kevin Bönisch, Alexander Mehler,
• Abstract summary: We introduce a retrieval approach leveraging Support Vector Regression ensembles, bootstrap aggregation (bagging), and embedding spaces on the German dataset for Legal Information Retrieval (GerDaLIR) We show improved recall over the baselines using our voting ensemble, suggesting promising initial results, without training or fine-tuning any deep learning models.
• Score: 51.0691253204425
• License:
• Abstract: We introduce a retrieval approach leveraging Support Vector Regression (SVR) ensembles, bootstrap aggregation (bagging), and embedding spaces on the German Dataset for Legal Information Retrieval (GerDaLIR). By conceptualizing the retrieval task in terms of multiple binary needle-in-a-haystack subtasks, we show improved recall over the baselines (0.849 > 0.803 | 0.829) using our voting ensemble, suggesting promising initial results, without training or fine-tuning any deep learning models. Our approach holds potential for further enhancement, particularly through refining the encoding models and optimizing hyperparameters.

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