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Automatic dominant character identification in fables based on verb analysis - Empirical study on the impact of anaphora resolution

机译：基于动词分析的寓言中的优势人物自动识别-回指解析影响的实证研究

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摘要

Named entity recognition (NER) is a subtask in information extraction which aims to locate atomic element into predefined types. Various NER techniques and tools have been developed to fit the interest of the applications developed. However, most NER works carried out focus on non-fiction domain. Fiction based domain displays a complex context in locating its NE, specifically whereby its characters could be represented in diverse spectrums, ranging from living things (animals, plants, and person) to non-living things (vehicle, furniture). Motivated by a hypothesis such that there always exists verb specifically describes human being conduct, in this paper, we propose a NER system which aims to identify NEs that perform human activity based on verb analysis (VAHA) in an autonomous manner. More specifically, our approach attempts to identify dominant character (DC) by studying the nature of verb that associates with human activity via TreeTagger, Stanford packages and WordNet. Our experimental results validate our initial hypothesis that NEs can be accurately identified by referring to the associated verbs that associate with human activity. Our empirical study also proves that the approach is applicable to small text size articles. Another significant contribution of our approach is that it does not require training data set and anaphora resolution.
机译：命名实体识别（NER）是信息提取中的子任务，旨在将原子元素定位为预定义的类型。已经开发了各种NER技术和工具来适应开发的应用程序的兴趣。但是，大多数NER的工作都集中在非小说领域。基于小说的域在定位其NE时显示了一个复杂的上下文，特别是通过它的角色可以在从生物（动物，植物和人）到非生物（车辆，家具）的各种光谱中表示。基于这样的假设，即总是存在动词来具体描述人类的行为，在本文中，我们提出了一个NER系统，该系统旨在基于动词分析（VAHA）以自主方式识别执行人类活动的NE。更具体地说，我们的方法尝试通过研究通过TreeTagger，Stanford软件包和WordNet与人类活动相关联的动词的性质来识别主导人物（DC）。我们的实验结果验证了我们最初的假设，即可以通过参考与人类活动相关的相关动词来准确识别NE。我们的实证研究也证明该方法适用于小文本尺寸的文章。我们方法的另一个重要贡献是它不需要训练数据集和回指解析度。

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