A Study about automatic domain term extraction from corpus

我们主要研究术语自动抽取的方法，即从NTCIR1 TMREC任务组分发的领域语料库中抽取领域特定术语。在各种术语自动抽取方面的研究中，大部分涉及语料库中的频度等统计问题，很少涉及抽取术语所构成的空间特征。在这项工作中，我们主要关注后者。我们提出的方法是利用复合名词之间的统计关系，复合名词占所有术语的85%，而简单名词占剩余的15%。例如，如果我们有许多复合名词，如“人类信息系统”、“社会信息系统”等等，“信息”的重要性被定义为有多少种名词与“信息”相邻或与之相连。然后，将复合名词的重要性定义为其组成名词的几何平均值。我们的系统包括(L)词法分析，(2)候选词提取，(3)为每个候选词赋予重要值，(4)使用NTCIR1 TMREC测试集进行评估。该方法在参与NTCIR1的方法中得分最高。我们还将我们的方法本地化为英语，以便明年研究翻译提取。

We mainly grappled with automatic term extraction methods which extracts domain specific terms from domain corpora that were distributed by NTCIR1 TMREC task group. Among various works in automatic term extraction, the majority of them are concerned with statistics like frequency in corpora, and few focused on the characteristics of space which consists of extracted terms. In this work, we mainly focus on the latter. We propose the method which uses the statistical relation between compound nouns, that are up to 85% of all terms and the remaining 15% of simple nouns. For instance, if we have many compound nouns such as "human information system", "social information system" and so on, the importance of "information" is defined as how many kinds of nouns adjoin or are adjoined with "information." Then, the importance of compound noun is defined as the geometric means of its component nouns. Our system consists of (l) morphological analysis, (2)extracting candidate terms, (3) assign each candidate term its importance value and (4) evaluation with NTCIR1 TMREC test collection. The proposed method shows the high score among methods participating NTCIR1. We also localize our method to English in order for translation extraction to be investigated the next year.