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摘要翻译：
大多数与单身有关的研究是作为确定寿命的更大努力的一部分进行的。因此，在术语提取这个小的子领域中很少有新奇的东西。此外，现有的工作大多是经验动机和派生的。我们提出了一种新的概率派生测度，不受时间性的任何影响，它提供了从解析文本中收集语言证据和从谷歌搜索引擎中收集统计证据的专用测度来测量单一性。我们使用1825个测试案例与现有的经验推导函数进行了比较研究，发现在精确度、查全率和准确性方面有所改善。
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英文标题：
《Determining the Unithood of Word Sequences using a Probabilistic
Approach》
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作者：
Wilson Wong, Wei Liu, Mohammed Bennamoun
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最新提交年份：
2008
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
Most research related to unithood were conducted as part of a larger effort for the determination of termhood. Consequently, novelties are rare in this small sub-field of term extraction. In addition, existing work were mostly empirically motivated and derived. We propose a new probabilistically-derived measure, independent of any influences of termhood, that provides dedicated measures to gather linguistic evidence from parsed text and statistical evidence from Google search engine for the measurement of unithood. Our comparative study using 1,825 test cases against an existing empirically-derived function revealed an improvement in terms of precision, recall and accuracy.
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PDF链接：
https://arxiv.org/pdf/0810.0139
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