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摘要翻译：
本文重点研究了一个系统WOLFIE（从解释的例子中学习单词），该系统从与语义表示配对的句子语料库中获得语义词典。所学的词汇由词组和意义表征组成。WOLFIE是一个集成系统的一部分，该系统学习将句子转换为表示形式，如逻辑数据库查询。实验结果证明了Wolfie在四种不同的自然语言中学习数据库接口有用词汇的能力。将WOLFIE学习的词汇的有用性与类似系统获得的词汇进行了比较，结果对WOLFIE有利。第二组实验证明了Wolfie有能力扩展到更大、更困难的语料库，尽管是人工生成的。在自然语言获取中，很难收集到监督学习所需的注释数据；然而，未注释的数据相当丰富。主动学习方法试图只选择信息最丰富的示例进行注释和训练，因此在自然语言应用中潜在地非常有用。然而，到目前为止，大多数主动学习的结果只考虑了标准分类任务。为了减少标注工作量，同时保持标注的准确性，我们将主动学习应用到语义词典中。我们表明，主动学习可以显著减少达到给定性能水平所需的注释示例的数量。
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英文标题：
《Acquiring Word-Meaning Mappings for Natural Language Interfaces》
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作者：
C. Thompson
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最新提交年份：
2011
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Computation and Language 计算与语言
分类描述：Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.
涵盖自然语言处理。大致包括ACM科目I.2.7类的材料。请注意，人工语言（编程语言、逻辑学、形式系统）的工作，如果没有明确地解决广义的自然语言问题（自然语言处理、计算语言学、语音、文本检索等），就不适合这个领域。
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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：
This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of phrases paired with meaning representations. WOLFIE is part of an integrated system that learns to transform sentences into representations such as logical database queries. Experimental results are presented demonstrating WOLFIE's ability to learn useful lexicons for a database interface in four different natural languages. The usefulness of the lexicons learned by WOLFIE are compared to those acquired by a similar system, with results favorable to WOLFIE. A second set of experiments demonstrates WOLFIE's ability to scale to larger and more difficult, albeit artificially generated, corpora. In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, most results to date for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to semantic lexicons. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance.
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PDF链接：
https://arxiv.org/pdf/1106.4571
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