Name Entity Disambiguation is the Natural Language Processing task of identifying textual records corresponding to the same Named Entity, i.e. real-world entities represented as a list of attributes (names, places, organisations, etc.). In this work, we face the task of disambiguating companies on the basis of their written names. We propose a Siamese LSTM Network approach to extract -- via supervised learning -- an embedding of company name strings in a (relatively) low dimensional vector space and use this representation to identify pairs of company names that actually represent the same company (i.e. the same Entity). Given that the manual labelling of string pairs is a rather onerous task, we analyse how an Active Learning approach to prioritise the samples to be labelled leads to a more efficient overall learning pipeline. With empirical investigations, we show that our proposed Siamese Network outperforms several benchmark approaches based on standard string matching algorithms when enough labelled data are available. Moreover, we show that Active Learning prioritisation is indeed helpful when labelling resources are limited, and let the learning models reach the out-of-sample performance saturation with less labelled data with respect to standard (random) data labelling approaches.
翻译:实体的模糊性是自然语言处理任务,即识别与同名实体相对的文本记录,即作为属性清单(名称、地点、组织等)所代表的真实世界实体。在这项工作中,我们面临着根据公司书面名称进行模糊化的任务。我们建议采用Siamsese LSTM网络方法,通过监督学习,提取公司名称字符串嵌在(相对的)低维矢量矢量空间中,并使用这种代表来识别实际代表同一公司(即同一实体)的公司名称配对。鉴于字符串配对的手工标签是一项相当繁重的任务,我们分析如何通过积极学习方法,根据标定标定标定标定样品的优先次序,导致更有效的总体学习管道。我们通过经验调查,我们发现我们提议的Siams网络在有足够标签数据时,在标准字符串匹配算法的基础上,超越了几种基准方法。此外,我们表明,当标签资源有限时,积极学习前置确实有帮助,让学习模式到达标定标准数据标签的外部标准,以不甚甚尊重数据。</s>