支持Web可访问性评估的语义内容分析

Carlos M. Duarte, Inês Matos, L. Carriço
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引用次数: 5

摘要

尽管它有很多优点,但自动的网页可访问性评估的主要限制仍然是它无法评估需要对网页内容进行语义理解的规则和技术。今天,机器学习解决方案可以以合理的置信度解释不同的媒体内容。这些解决方案在增加网页的可访问性方面具有未开发的潜力。这种潜力也延伸到网页的评估。本文提出了一种在网页可访问性评估环境中,对内容与其文本描述之间的相似度进行自动评级的算法。通过比较该算法对从所有文本中获得的图像描述的评级与人类对相同描述的评级,证明了该算法的有效性。
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Semantic Content Analysis Supporting Web Accessibility Evaluation
Despite its many advantages, automated web accessibility evaluation's main limitation is still its inability to assess rules and techniques that require a semantic understanding of the web content. Today, machine learning solutions are available that can interpret different media content with a reasonable degree of confidence. These solutions have an untapped potential to increase the accessibility of web pages. This potential extends to the evaluation of web pages also. This paper proposes an algorithm to automatically rate the similarity between a content and its textual description in a web accessibility evaluation context. The validity of the algorithm is demonstrated by comparing its ratings of descriptions of images obtained from their alt texts with human ratings of the same descriptions.
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