艺术品命名实体识别训练数据的生成

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Semantic Web Pub Date : 2022-08-08 DOI:10.3233/sw-223177
Nitisha Jain, Alejandro Sierra-Múnera, Jan Ehmueller, Ralf Krestel
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引用次数: 1

摘要

随着机器学习技术越来越多地用于文本处理任务,对训练数据的需求已成为其应用的主要瓶颈。人工生成适合每个任务的大规模训练数据集是一个耗时且昂贵的过程,因此需要自动生成。在这项工作中,我们将注意力转向文化遗产领域背景下命名实体识别(NER)的训练数据集的创建。NER在许多自然语言处理系统中起着重要的作用。大多数NER系统通常仅限于几个常见的命名实体类型,如人员、位置和组织。然而,对于数字化艺术档案等文化遗产资源而言,艺术品名称等细粒度实体类型的识别非常重要。由于缺乏相关的训练数据集,目前最先进的工具无法充分识别艺术品标题。我们分析了该领域提出的特殊困难,并激发了对高质量注释的需求,以训练机器学习模型来识别艺术品标题。我们提出了一个基于启发式方法的框架,通过利用来自Wikidata等知识库的现有文化遗产资源来创建高质量的训练数据。实验评估显示,当模型在使用我们的框架生成的数据集上进行训练时,艺术品标题的NER性能比基线有了显著改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generation of training data for named entity recognition of artworks
As machine learning techniques are being increasingly employed for text processing tasks, the need for training data has become a major bottleneck for their application. Manual generation of large scale training datasets tailored to each task is a time consuming and expensive process, which necessitates their automated generation. In this work, we turn our attention towards creation of training datasets for named entity recognition (NER) in the context of the cultural heritage domain. NER plays an important role in many natural language processing systems. Most NER systems are typically limited to a few common named entity types, such as person, location, and organization. However, for cultural heritage resources, such as digitized art archives, the recognition of fine-grained entity types such as titles of artworks is of high importance. Current state of the art tools are unable to adequately identify artwork titles due to unavailability of relevant training datasets. We analyse the particular difficulties presented by this domain and motivate the need for quality annotations to train machine learning models for identification of artwork titles. We present a framework with heuristic based approach to create high-quality training data by leveraging existing cultural heritage resources from knowledge bases such as Wikidata. Experimental evaluation shows significant improvement over the baseline for NER performance for artwork titles when models are trained on the dataset generated using our framework.
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来源期刊
Semantic Web
Semantic Web COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
8.30
自引率
6.70%
发文量
68
期刊介绍: The journal Semantic Web – Interoperability, Usability, Applicability brings together researchers from various fields which share the vision and need for more effective and meaningful ways to share information across agents and services on the future internet and elsewhere. As such, Semantic Web technologies shall support the seamless integration of data, on-the-fly composition and interoperation of Web services, as well as more intuitive search engines. The semantics – or meaning – of information, however, cannot be defined without a context, which makes personalization, trust, and provenance core topics for Semantic Web research. New retrieval paradigms, user interfaces, and visualization techniques have to unleash the power of the Semantic Web and at the same time hide its complexity from the user. Based on this vision, the journal welcomes contributions ranging from theoretical and foundational research over methods and tools to descriptions of concrete ontologies and applications in all areas. We especially welcome papers which add a social, spatial, and temporal dimension to Semantic Web research, as well as application-oriented papers making use of formal semantics.
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