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A simple approach for Monolingual Event Tracking system in Bengali

机译:孟加拉语单语事件跟踪系统的一种简单方法

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Real-world applications have to take into consideration both topics and sentiments for precise opinion measurement. Topic and sentiment alignment is crucial for opinion retrieval, extraction, categorization, and aggregation on various issues. In this paper, we have reported a Monolingual Event (or, topic) Tracking system for Bengali. The system has been developed based on a newspaper corpus developed from the web archive of a leading Bengali newspaper. The goal of the system is to determine whether two news documents within a range of dates describe the same event. An event is a vector consisting of person, location, organization, title and date. A particular news document is described as a collection of such event vectors. A particular threshold value has been considered to check whether the number of event vectors of two separate news documents match at least by this threshold. Any particular news document of a date has been selected as the initial story. All the news documents within the preceding 15 and following 15 days have been considered as the target stories (or, documents). Evaluation results have demonstrated the Recall and Precision of 58.93% and 84.62%, respectively. The future works will look for interactions between topics and associated sentiments.
机译:实际应用程序必须同时考虑主题和情感,以进行精确的意见评估。主题和情感的一致性对于各种问题的观点检索,提取,分类和汇总至关重要。在本文中,我们报告了孟加拉语的单语事件(或主题)跟踪系统。该系统是根据报纸语料库开发的,该语料库是从孟加拉语主要报纸的网络档案库中开发出来的。该系统的目标是确定日期范围内的两个新闻文档是否描述了同一事件。事件是一个由人,位置,组织,标题和日期组成的向量。特定的新闻文档被描述为此类事件向量的集合。已经考虑了特定的阈值以检查两个单独的新闻文档的事件向量的数量是否至少匹配该阈值。日期的任何特定新闻文档都已被选为初始故事。前15天和后15天之内的所有新闻文档均被视为目标新闻(或文档)。评估结果表明召回率和精确度分别为58.93%和84.62%。未来的作品将寻找主题与相关情感之间的互动。

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