Extraction of Named Entities from Social Media Text in Tamil Language Using N-Gram Embedding for Disaster Management
Extraction of Named Entities from Social Media Text in Tamil Language Using N-Gram Embedding for Disaster Management
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使用 N-Gram 嵌入从泰米尔语社交媒体文本中提取命名实体进行灾害管理
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
K. Soman
中科院分区：
文献类型：
--
作者：
G. R. Devi;M. A. Kumar;K. Soman
在当今时代，任何形式的数据都被认为是更重要的。更具体地说，文本数据比任何其他形式的数据都具有丰富而简短的信息。对这些数据的提取和分析可以通过文本分析产生各种新的发现。这导致了搜索引擎、产品名称提取、情感分析、文档分类等应用程序的出现。公司非常注重情感分析，以评估对其产品的正面、负面和中性的评论。文本摘要是自然语言处理的一个值得注意的应用，它揭示了简短文档的精髓。除此之外，在关注社会福利方面，可以开发基于信息提取的应用程序。处理紧急情况需要收集大量信息。这种数据的提取可以在灾害管理期间起到支持作用。为了感知这样的任务，系统必须学习人类语言的意义。自然语言处理(NLP)系统的主要目的是简化文本数据跨越语言障碍的可访问性。该系统利用词嵌入模型，特别是跳过语法模型来实现自然语言处理的最基本任务--社交媒体文本中的实体提取。N元语法嵌入方法的实现为系统处理社交媒体文本创建丰富的上下文知识铺平了道路。利用机器学习分类器支持向量机对命名实体进行了分类。
In the present era, data in any form is considered with greater importance. More specifically, text data has rich and brief information than any other form of data. Extraction and analysis of these data can result in various new findings through text analytics. This has led to applications such as search engines, extraction of product names, sentiment analysis, document classification and few more. Companies are much focused on sentimental analysis to review the positive, negative and neutral comments for their products. Summarization of text is a notable application of Natural Language Processing that reveals the gist of brief documents. Apart from these, on concerning welfare of the society, application based on information extraction can be developed. Handling an emergency situation requires collection of vast information. Extraction of such data can be supportive during disaster management. In order to perceive such task, system must learn the meaning of human languages. To ease the accessibility of text data across language barriers is the primary motive of Natural Language Processing (NLP) systems. The proposed systems has utilized word embedding model, specifically skip gram model to implement the most fundamental task of NLP—entity extraction in social media text. Implementation of N-gram embedding methods paved way for creation of rich context knowledge for the system to handle social media text. Classification of named entities using the proposed system has been carried out using machine learning classifier Support Vector Machine (SVM).
影响因子：
8.6
作者：
Derczynski, Leon;Maynard, Diana;Bontcheva, Kalina
通讯作者：
Bontcheva, Kalina
回复：“使用数值方法设计模拟：重新审视平衡截距”。
影响因子：
5
作者：
Zivich,PaulN;Ross,RachaelK
通讯作者：
Ross,RachaelK