Seki Kazuhiro, Mostafa Javed
Laboratory of Applied Informatics Research, Indiana University, Bloomington, 47405-3907, USA.
Proc IEEE Comput Soc Bioinform Conf. 2003;2:251-8.
This paper proposes a method for identifying protein names in biomedical texts with an emphasis on detecting protein name boundaries. We use a probabilistic model which exploits several surface clues characterizing protein names and incorporates word classes for generalization. In contrast to previously proposed methods, our approach does not rely on natural language processing tools such as part-of-speech taggers and syntactic parsers, so as to reduce processing overhead and the potential number of probabilistic parameters to be estimated. A notion of certainty is also proposed to improve precision for identification. We implemented a protein name identification system based on our proposed method, and evaluated the system on real-world biomedical texts in conjunction with the previous work. The results showed that overall our system performs comparably to the state-of-the-art protein name identification system and that higher performance is achieved for compound names. In addition, it is demonstrated that our system can further improve precision by restricting the system output to those names with high certainties.
本文提出了一种在生物医学文本中识别蛋白质名称的方法，重点在于检测蛋白质名称的边界。我们使用一种概率模型，该模型利用了表征蛋白质名称的几个表面线索，并纳入词类进行泛化。与先前提出的方法不同，我们的方法不依赖诸如词性标注器和句法分析器等自然语言处理工具，以减少处理开销和待估计的概率参数数量。还提出了确定性的概念以提高识别的精度。我们基于所提出的方法实现了一个蛋白质名称识别系统，并结合先前的工作在真实世界的生物医学文本上对该系统进行了评估。结果表明，总体而言我们的系统与最先进的蛋白质名称识别系统性能相当，并且对于复合名称有更高的性能表现。此外，还证明了我们的系统可以通过将系统输出限制为具有高确定性的名称来进一步提高精度。
