工程延误纠纷文档审查中文本分类场景下嵌入模型的实证评估
Empirical Evaluation of Embedding Models in the Context of Text Classification in Document Review in Construction Delay Disputes
AI总结：
针对工程延误纠纷文档审查中的文本分类需求，对比评估四种嵌入模型结合KNN、LR的二分类效果，验证了其提升法律文档分析效率与准确性的潜力。
AI中文摘要：
文本嵌入是文本数据的数值表示形式，即将单词、短语或整篇文档转换为实数向量。这类嵌入能够在连续向量空间中捕捉文本元素的语义含义与关联关系。文本嵌入的核心目标是让需要数值输入的机器学习模型能够处理文本数据。目前已有大量面向不同应用场景的嵌入模型被开发出来。本文通过对四种不同模型开展全面对比分析，评估不同嵌入模型的文本分类效果。我们同时采用K-Nearest Neighbors（KNN，K近邻）和Logistic Regression（LR，逻辑回归）执行二分类任务，具体是在一个标注数据集中判断文本片段是否与“延误”相关。本研究探索了利用文本片段嵌入训练有监督文本分类模型，以在工程延误纠纷的文档审查流程中识别延误相关表述。研究结果凸显了嵌入模型提升法律场景下文档分析效率与准确性的潜力，为复杂调查场景中更明智的决策奠定了基础。
英文摘要：
Text embeddings are numerical representations of text data, where words, phrases, or entire documents are converted into vectors of real numbers. These embeddings capture semantic meanings and relationships between text elements in a continuous vector space. The primary goal of text embeddings is to enable the processing of text data by machine learning models, which require numerical input. Numerous embedding models have been developed for various applications. This paper presents our work in evaluating different embeddings through a comprehensive comparative analysis of four distinct models, focusing on their text classification efficacy. We employ both K-Nearest Neighbors (KNN) and Logistic Regression (LR) to perform binary classification tasks, specifically determining whether a text snippet is associated with 'delay' or 'not delay' within a labeled dataset. Our research explores the use of text snippet embeddings for training supervised text classification models to identify delay-related statements during the document review process of construction delay disputes. The results of this study highlight the potential of embedding models to enhance the efficiency and accuracy of document analysis in legal contexts, paving the way for more informed decision-making in complex investigative scenarios.
