Word pattern prediction using Big Data frameworks

IF 0.3 Q4 COMPUTER SCIENCE, THEORY & METHODS
B. Szabari, A. Kiss
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引用次数: 1

Abstract

Abstract Using software applications or services, which provide word or even word pattern recommendation service has become part of our lives. Those services appear in many form in our daily basis, just think of our smartphones keyboard, or Google search suggestions and this list can be continued. With the help of these tools, we can not only find the suitable word that fits into our sentence, but we can also express ourselves in a much more nuanced, diverse way. To achieve this kind of recommendation service, we use an algorithm which is capable to recommend word by word pattern queries. Word pattern queries, can be expressed as a combination of words, part-of-speech (POS) tags and wild card words. Since there are a lot of possible patterns and sentences, we use Big Data frameworks to handle this large amount of data. In this paper, we compared two popular framework Hadoop and Spark with the proposed algorithm and recommend some enhancement to gain faster word pattern generation.
使用大数据框架的词模式预测
摘要利用软件应用程序或服务,提供文字甚至文字模式推荐服务已经成为我们生活的一部分。这些服务以多种形式出现在我们的日常生活中,只要想想我们的智能手机键盘,或者谷歌搜索建议,这个列表还可以继续。在这些工具的帮助下,我们不仅可以找到适合我们句子的合适单词,而且我们还可以用一种更微妙、更多样化的方式表达自己。为了实现这种推荐服务,我们使用了一种能够逐字推荐模式查询的算法。单词模式查询可以表示为单词、词性(POS)标记和通配符单词的组合。由于有很多可能的模式和句子,我们使用大数据框架来处理这大量的数据。在本文中,我们将两种流行的框架Hadoop和Spark与所提出的算法进行了比较,并提出了一些改进建议,以获得更快的单词模式生成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Acta Universitatis Sapientiae Informatica
Acta Universitatis Sapientiae Informatica COMPUTER SCIENCE, THEORY & METHODS-
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发文量
9
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