Mention detection is an important component of coreference resolution system, where mentions such as name, nominal, and pronominals are identified. These mentions can be purely coreferential mentions or singleton mentions (non-coreferential mentions). Coreferential mentions are those mentions in a text that refer to the same entities in a real world. Whereas, singleton mentions are mentioned only once in the text and do not participate in the coreference as they are not mentioned again in the following text. Filtering of these singleton mentions can substantially improve the performance of a coreference resolution process. This paper proposes a singleton mention detection module based on a fully connected network and a Convolutional neural network for Hindi text. This model utilizes a few hand-crafted features and context information, and word embedding for words. The coreference annotated Hindi dataset comprising of 3.6K sentences, and 78K tokens are used for the task. In terms of Precision, Recall, and F-measure, the experimental findings obtained are excellent.
翻译:引用识别是共同参考解析系统的一个重要部分,其中提及的名称、名义和名词等,这些提及可以是纯共同提及或单一提及(非共同提及)。在提及真实世界中相同实体的文本中提及共同提及。在文本中仅提及一次,不参与共同引用,因为以下文本中未提及。过滤这些单独提及可大大改善共同引用解析进程的绩效。本文件提议使用一个单一提及识别模块,该模块基于一个完全连接的网络和印地文的动态神经网络。该模型使用几个手工制作的特征和背景信息,并用字嵌入文字。在任务中使用了由3.6K句组成的附加印地语数据集和78K标语的索引。在Precision、Recall和F-度方面,获得的实验结果是极好的。