Accurately typing entity mentions from text segments is a fundamental task for various natural language processing applications. Many previous approaches rely on massive human-annotated data to perform entity typing. Nevertheless, collecting such data in highly specialized science and engineering domains (e.g., software engineering and security) can be time-consuming and costly, without mentioning the domain gaps between training and inference data if the model needs to be applied to confidential datasets. In this paper, we study the task of seed-guided fine-grained entity typing in science and engineering domains, which takes the name and a few seed entities for each entity type as the only supervision and aims to classify new entity mentions into both seen and unseen types (i.e., those without seed entities). To solve this problem, we propose SEType which first enriches the weak supervision by finding more entities for each seen type from an unlabeled corpus using the contextualized representations of pre-trained language models. It then matches the enriched entities to unlabeled text to get pseudo-labeled samples and trains a textual entailment model that can make inferences for both seen and unseen types. Extensive experiments on two datasets covering four domains demonstrate the effectiveness of SEType in comparison with various baselines.

翻译：准确地对文本中的实体提及进行类型标注是自然语言处理应用中的基础任务。以往方法大多依赖大量人工标注数据实现实体类型识别。然而，在高度专业化的科学与工程领域（如软件工程、网络安全）收集此类数据耗时且昂贵，更遑论当模型需应用于保密数据集时训练数据与推理数据存在的领域差异问题。本文研究科学与工程领域中种子引导的细粒度实体类型标注任务——该任务仅以每个实体类型的名称和少量种子实体作为监督信号，旨在将新实体提及分类为已知类型与未知类型（即未提供种子实体的类型）。为解决该问题，我们提出SEType方法：首先利用预训练语言模型的上下文表征从未标注语料中为每个已知类型挖掘更多实体以增强弱监督信号，继而将扩充后的实体与未标注文本进行匹配获取伪标注样本，并训练能够对已知及未知类型进行推理的文本蕴含模型。在覆盖四个领域的两组数据集上的大量实验表明，SEType相较各类基线方法具有显著有效性。