利用语言模型从印度史诗《摩诃婆罗多》中计算估计相关词
Estimating related words computationally using language model from the Mahabharata -- an Indian epic
AI总结：
本文提出一个自然语言处理流水线，从印度史诗《摩诃婆罗多》中获取统计与计算见解，并实现最相关词的搜索方法。
摘要：
《摩诃婆罗多》是众多印度文学作品中最为流行的一部，在许多领域中被引用，用于完全不同的目的。该文本本身具有多个维度和方面，对人类的个人生活和职业生活都有用处。这部印度史诗最初以梵语写成。如今，在自然语言处理、人工智能、机器学习和人机交互的时代，可以根据领域需求对该文本进行处理。处理该文本并从《摩诃婆罗多》中获取有用的见解是很有趣的。人类在分析《摩诃婆罗多》时的局限性在于，他们总是对作者叙述的故事带有情感方面的倾向。除此之外，人类无法记住统计或计算细节，例如哪两个词经常出现在一个句子中？整个文献中句子的平均长度是多少？哪个词是文本中最流行的词，句子中使用的词的词元是什么？因此，在本文中，我们提出了一个自然语言处理流水线，以获取一些统计和计算见解，以及从最大的史诗《摩诃婆罗多》中搜索最相关词的方法。我们堆叠了不同的文本处理方法，以表达最佳结果，这些结果可进一步用于需要引用《摩诃婆罗多》的各种领域。
英文摘要：
'Mahabharata' is the most popular among many Indian pieces of literature referred to in many domains for completely different purposes. This text itself is having various dimension and aspects which is useful for the human being in their personal life and professional life. This Indian Epic is originally written in the Sanskrit Language. Now in the era of Natural Language Processing, Artificial Intelligence, Machine Learning, and Human-Computer interaction this text can be processed according to the domain requirement. It is interesting to process this text and get useful insights from Mahabharata. The limitation of the humans while analyzing Mahabharata is that they always have a sentiment aspect towards the story narrated by the author. Apart from that, the human cannot memorize statistical or computational details, like which two words are frequently coming in one sentence? What is the average length of the sentences across the whole literature? Which word is the most popular word across the text, what are the lemmas of the words used across the sentences? Thus, in this paper, we propose an NLP pipeline to get some statistical and computational insights along with the most relevant word searching method from the largest epic 'Mahabharata'. We stacked the different text-processing approaches to articulate the best results which can be further used in the various domain where Mahabharata needs to be referred.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
