Abstract
For Relation Extraction (RE), the manual annotation of training data may be prohibitively expensive, since the sentences that contain the target relations in texts can be very scarce and difficult to find. It is therefore beneficial to develop an efficient method that can automatically extract training instances from unlabeled texts for training RE models. Recently, large language models (LLMs) have been adopted in various natural language processing tasks, with RE also benefiting from their advances. However, when leveraging LLMs for RE with predefined relation categories, two key challenges arise. First, in a multi-class classification setting, LLMs often struggle to comprehensively capture the semantics of every relation, leading to suboptimal results. Second, although employing binary classification for each relation individually can mitigate this issue, it introduces significant computational overhead, resulting in impractical time complexity for real-world applications. Therefore, this paper proposes a framework called M-BRe to extract training instances from unlabeled texts for RE. It utilizes three modules to combine the advantages of both of the above classification approaches: Relation Grouping, Relation Extraction, and Label Decision. Extensive experiments confirm its superior capability in discovering high-quality training samples from unlabeled texts for RE.
Abstract (translated)
对于关系抽取（RE），训练数据的手动标注可能成本极高，因为文本中包含目标关系的句子可能会非常稀少且难以找到。因此，开发一种可以从未标记文本中自动提取训练实例的方法以用于训练RE模型是有益的。最近，大型语言模型（LLMs）已被应用于各种自然语言处理任务，并且RE也从中受益匪浅。然而，在利用LLMs进行关系抽取时，特别是在使用预定义的关系类别的情况下，会出现两个关键挑战。首先，在多类分类设置中，LLMs往往难以全面捕捉每种关系的语义，从而导致次优结果。其次，尽管对每个关系单独采用二元分类可以缓解这一问题，但这种方法会引入显著的计算开销，并导致实际应用中的时间复杂度不可行。因此，本文提出了一种名为M-BRe的框架，用于从未标记文本中提取训练实例以进行RE。该框架利用三个模块：关系分组、关系抽取和标签决策，结合上述两种分类方法的优点。广泛的实验验证了其在从未标记文本中发现高质量训练样本方面的卓越能力。
URL
https://arxiv.org/abs/2509.07730
