Sevenster Merlijn, Aleksovski Zharko
Philips Research, Eindhoven, The Netherlands.
AMIA Annu Symp Proc. 2010 Nov 13;2010:742-6.
In many applications autocompletion functionality saves keystrokes, increases user experience, and helps the user to comply with standardized terminology. Intuitively, the more context information we have about the user, the more accurate autocompletion suggestions we can give. In this paper we research the added value of contextual information for autocompletion algorithms, measured as the average number of saved keystrokes. In our experiments, a context is represented as a set of SNOMED CT terms. Using the structure of SNOMED CT we determine the semantic distance of each SNOMED CT term to the context terms. The resulting distance function is injected in the autocompletion algorithms to reward terms that are semantically close to the context. Our results show that semantic enhancement saves up to 18% of keystrokes, in addition to the percentage of keystrokes saved for the non-semantic base algorithm.
在许多应用中，自动完成功能节省了击键次数，提升了用户体验，并帮助用户遵循标准化术语。直观地说，我们拥有的关于用户的上下文信息越多，就能给出越准确的自动完成建议。在本文中，我们研究上下文信息对于自动完成算法的附加价值，以节省的平均击键次数来衡量。在我们的实验中，上下文由一组SNOMED CT术语表示。利用SNOMED CT的结构，我们确定每个SNOMED CT术语与上下文术语的语义距离。由此产生的距离函数被注入到自动完成算法中，以奖励那些在语义上与上下文接近的术语。我们的结果表明，除了非语义基础算法节省的击键百分比之外，语义增强还能节省高达18%的击键次数。
