将自组织映射与基于记忆的学习相结合
Combining a self-organising map with memory-based learning
摘要：
基于记忆的学习（MBL）在基于语料库的自然语言处理（NLP）任务中取得了相当大的成功，因此是构建基于语料库的NLP系统时获得高水平性能的可靠方法。然而，MBL存在一个瓶颈，即任何新的测试项目都必须与记忆库中的所有训练项目进行比较。由于这个原因，人们对各种形式的记忆编辑产生了一些兴趣，从而采用某种选择记忆库子集的方法来减少比较的次数。本文探讨了使用修改后的自组织映射（SOM）选择一个子集的记忆项目进行比较。该方法涉及将比较的数量减少到与训练项目数量的平方根成比例的值。该方法在华尔街日报语料库中的基本名词短语识别上进行了测试，使用第15至18节进行训练，第20节进行测试。
英文摘要：
Memory-based learning (MBL) has enjoyed considerable success in corpus-based natural language processing (NLP) tasks and is thus a reliable method of getting a high-level of performance when building corpus-based NLP systems. However there is a bottleneck in MBL whereby any novel testing item has to be compared against all the training items in memory base. For this reason there has been some interest in various forms of memory editing whereby some method of selecting a subset of the memory base is employed to reduce the number of comparisons. This paper investigates the use of a modified self-organising map (SOM) to select a subset of the memory items for comparison. This method involves reducing the number of comparisons to a value proportional to the square root of the number of training items. The method is tested on the identification of base noun-phrases in the Wall Street Journal corpus, using sections 15 to 18 for training and section 20 for testing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
