基于深度神经网络的诗词检索

Neural Network-Based Poetry Retrieval

中国古典诗词是中国古典文学的代表之一,是中华传统文化的宝藏,源远流长。中国古典诗词研究是自然语言处理方向的一项重要且富有意义的工作。随着人工智能的发展,人工神经网络在图像、文本等领域得到广泛的应用,取得了显著的突破,给人工智能与中国古典诗词相结合提供了新的思路和方法。让机器去理解中国古典诗词的韵律和意境是一项极具挑战的工作,其中,通过研究诗词的相似性来提升机器对诗词的理解这一研究课题被赋予了更为重要的意义。诗词检索是对诗词内容做对比,查找出在语义和意境上相接近的诗词,这要求对整首诗词的内容和意境有深入的理解。该文模型以数十万首古诗作为基础,利用循环神经网络(RNN)自动学习古诗句的语义表示,并设计了多种方法自动计算两首诗之间的关联性,以此计算两首诗词之间的语义距离,实现诗词的推荐。自动评测和人工评测的实验结果都表明,该文模型能够生成质量较好的诗词检索结果。

Chinese classical poetry,with its long history,is one of the representatives of Chinese classical literature and a treasure of Chinese traditional culture.Poetry retrieval is a comparison of the content between poetry,finding poems that are similar in semantics and artistic conception,which demands requires an in-depth understanding of the content and mood of the whole poem.This paper applies the recurrent neural network(RNN)to automatically learn the semantic representation of ancient poems.A variety of methods is designed to automatically calculate the correlation between two poems to further calculate the semantic distance between them,achieving the recommendation of poetry.The experimental results of automatic and manual evaluation show that the model can generate good quality poetry retrieval results.