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With the development of Internet, crowdsourcing short texts such as time-sync comments for videos are of significant importance for online media sharing platforms and leisure industry. It also provides a new research opportunity for the evolution of recommender system, artificial intelligence and so on, which have tremendous values for every walk of life. At the same time, there are many challenges for crowdsourcing short text analysis, because of its high noise, non-standard expressions and latent semantic implication. These have limited the application of traditional natural language processing (NLP) techniques, thus it needs a novel short text understanding method which is of high fault tolerance, and can capture the deep semantics. To this end, this paper proposes a deep semantic representation model based on recurrent neural network (RNN). It can avoid the effect of noise on text segmentation by exploiting the character-based RNN. To achieve the semantic representation, we apply the neural network to represent the latent semantics such that the outputted semantic vectors can deeply reflect the time-sync comments. Then we further design a time-sync comment explanation framework based on semantic retrieval, used for the validation of semantic representation. Finally, we compare them with others baselines, and apply many measures to validate the proposed model. The experimental results show that model can capture the semantics in these short texts more precisely, and the assumptions related to time-sync comments are reasonable.
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非常不错的资源，谢谢分享
谢谢楼主分享
好资源，必须支持楼主。
真的很不错的干货，我很喜欢这个视频
58期是老男孩亲自讲的，感觉听他讲的听的舒服些。
谢谢分享
看看
收藏，学习学习，谢谢！
厉害
想看
果断MARK，前十有我必火！
感谢分享
很好很强大
谢谢分享#######
谢谢分享
我就来看看
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