【目的】对跨语言情感分析的研究脉络进行梳理总结。【文献范围】以Web of Science数据库为检索平台,以TS=cross lingual sentiment OR cross lingual word embedding为检索式,筛选90篇文献进行述评。【方法】根据跨语言情感分析所采用...【目的】对跨语言情感分析的研究脉络进行梳理总结。【文献范围】以Web of Science数据库为检索平台,以TS=cross lingual sentiment OR cross lingual word embedding为检索式,筛选90篇文献进行述评。【方法】根据跨语言情感分析所采用的技术进行分类概述,包括基于机器翻译及其改进、基于平行语料库、基于双语情感词典三种早期的主要方法,再到引入Word2Vec和GolVe等词向量模型后,基于跨语言词向量模型的方法,最后到2019年以来基于Multi-BERT等预训练模型的方法。【结果】总结跨语言情感分析相关研究的主要思路、方法模型、不足之处等,分析现有研究覆盖的语言、数据集及其性能。发现虽然Multi-BERT等预训练模型在零样本的跨语言情感分析上取得较好性能,但是仍然存在语言敏感性问题。早期的跨语言情感分析方法对现有研究仍有一定指导和参考价值。【局限】部分跨语言情感分析模型属于混合模型,分类时仅按照主要方法进行归纳。【结论】展望跨语言情感分析的未来发展和亟待解决的问题。随着预训练模型对多语言语义的深层次挖掘,适用于更多更广泛语种的跨语言情感分析模型将是未来发展方向。展开更多
Real-life events are emerging and evolving in social and news streams.Recent methods have succeeded in capturing designed features of monolingual events,but lack of interpretability and multi-lingual considerations.To...Real-life events are emerging and evolving in social and news streams.Recent methods have succeeded in capturing designed features of monolingual events,but lack of interpretability and multi-lingual considerations.To this end,we propose a multi-lingual event mining model,namely MLEM,to automatically detect events and generate evolution graph in multilingual hybrid-length text streams including English,Chinese,French,German,Russian and Japanese.Specially,we merge the same entities and similar phrases and present multiple similarity measures by incremental word2vec model.We propose an 8-tuple to describe event for correlation analysis and evolution graph generation.We evaluate the MLEM model using a massive human-generated dataset containing real world events.Experimental results show that our new model MLEM outperforms the baseline method both in efficiency and effectiveness.展开更多
文摘【目的】对跨语言情感分析的研究脉络进行梳理总结。【文献范围】以Web of Science数据库为检索平台,以TS=cross lingual sentiment OR cross lingual word embedding为检索式,筛选90篇文献进行述评。【方法】根据跨语言情感分析所采用的技术进行分类概述,包括基于机器翻译及其改进、基于平行语料库、基于双语情感词典三种早期的主要方法,再到引入Word2Vec和GolVe等词向量模型后,基于跨语言词向量模型的方法,最后到2019年以来基于Multi-BERT等预训练模型的方法。【结果】总结跨语言情感分析相关研究的主要思路、方法模型、不足之处等,分析现有研究覆盖的语言、数据集及其性能。发现虽然Multi-BERT等预训练模型在零样本的跨语言情感分析上取得较好性能,但是仍然存在语言敏感性问题。早期的跨语言情感分析方法对现有研究仍有一定指导和参考价值。【局限】部分跨语言情感分析模型属于混合模型,分类时仅按照主要方法进行归纳。【结论】展望跨语言情感分析的未来发展和亟待解决的问题。随着预训练模型对多语言语义的深层次挖掘,适用于更多更广泛语种的跨语言情感分析模型将是未来发展方向。
基金This work was supported by NSFC program(Grant Nos.61872022,61421003,U1636123)SKLSDE-2018ZX-16 and partly by the Beijing Advanced Innovation Center for Big Data and Brain Computing.
文摘Real-life events are emerging and evolving in social and news streams.Recent methods have succeeded in capturing designed features of monolingual events,but lack of interpretability and multi-lingual considerations.To this end,we propose a multi-lingual event mining model,namely MLEM,to automatically detect events and generate evolution graph in multilingual hybrid-length text streams including English,Chinese,French,German,Russian and Japanese.Specially,we merge the same entities and similar phrases and present multiple similarity measures by incremental word2vec model.We propose an 8-tuple to describe event for correlation analysis and evolution graph generation.We evaluate the MLEM model using a massive human-generated dataset containing real world events.Experimental results show that our new model MLEM outperforms the baseline method both in efficiency and effectiveness.
