有监督评价对象抽取总结（Aspect Extraction）

定义

定义：从文本中抽取所评价的实体和属性[1]。
在最初的商品评价的研究发展中称为“product feature”，随着任务的细化，出现了aspect，target等说法，中文中有翻译为方面级，属性级等说法，其本质为针对特定的评价词所对应的评价对象。

基于规则的抽取方法

1. DeJong, G. 1982. An Overview of the FRUMP System. Strategies for Natural Language Parsing. pages 149-176.
2. Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 168–177.
3. Li Zhuang, Feng Jing, and Xiao-Yan Zhu. 2006. Movie review mining and summarization. In Proceedings of the ACM 15th Conference on Information and Knowledge Management, pages 43–50, Arlington, Virginia, USA.
4. Jason Kessler and Nicolas Nicolov. 2009. Targeting sentiment expressions through supervised ranking of linguistic configurations. In Proceedings of the Third International AAAI Conference on Weblogs and Social Media, pages 90–97, San Jose, California, USA.
5. Guang Qiu, Bing Liu, Jiajun Bu, and Chun Chen. Opinion word expansion and target extraction through double propagation. Computational Linguistics, 37(1):9–27, 2011.
6. Soujanya Poria, Erik Cambria, Lun-Wei Ku, Chen Gui,and Alexander Gelbukh. 2014. A rule-based approach to aspect extraction from product reviews. In Proceedings of the second workshop on natural language processing for social media (SocialNLP). pages 28–37.
7. Qian Liu, Zhiqiang Gao, Bing Liu, and Yuanlin Zhang. 2015b. Automated rule selection for aspect extraction in opinion mining. In Proceedings of the 24th International Conference on Artificial Intelligence, pages 1291–1297. AAAI Press.

基于机器学习的抽取方法

1. Jason Kessler and Nicolas Nicolov. 2009. Targeting sentiment expressions through supervised ranking of linguistic configurations. In Proceedings of the Third International AAAI Conference on Weblogs and Social Media, pages 90–97, San Jose, California, USA
2. Wei Jin, Hung Hay Ho, and Rohini K Srihari. 2009. A novel lexicalized hmm-based learning framework for web opinion mining. In Proceedings of 2009 International Conference on Machine Learning, pages 465–472.
3. Niklas Jakob and Iryna Gurevych. 2010. Extracting opinion targets in a single-and cross-domain setting with conditional random fields. In Proceedings of the 2010 conference on empirical methods in natural language processing, pages 1035–1045.
4. Fangtao Li, Chao Han, Minlie Huang, Xiaoyan Zhu, Ying-Ju Xia, Shu Zhang, and Hao Yu. 2010. Structure-aware review mining and summarization. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistic, pages 653–661.
5. Asha S Manek, P Deepa Shenoy, M Chandra Mohan, and KR Venugopal. 2017. Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and svm classifier. World Wide Web Journal, 20(2):135–154
6. Lei Shu, Hu Xu, and Bing Liu. 2017. Lifelong learning crf for supervised aspect extraction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), volume 2, pages 148–154.[Notes]

基于深度学习的抽取方法

1. Zhiqiang Toh and Jian Su. 2016. Nlangp at semeval- 2016 task 5: Improving aspect based sentiment analysis using neural network features. In Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), pages 282–288.
2. Soujanya Poria, Erik Cambria, and Alexander Gelbukh. 2016. Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems, pages 42–49.
3. Hu Xu, Bing Liu, Lei Shu, and Philip S Yu. 2018. Double embeddings and cnn-based sequence labeling for aspect extraction. arXiv preprint arXiv:1805.04601.[Notes]
4. Pengfei Liu, Shafiq Joty, and Helen Meng. 2015. Finegrained opinion mining with recurrent neural networks and word embeddings. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1433–1443.
5. Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, and Xiaokui Xiao. 2016. Recursive neural conditional random fields for aspect-based sentiment analysis. arXiv preprint arXiv:1603.06679.
6. Xin Li and Wai Lam. 2017. Deep multi-task learning for aspect term extraction with memory interaction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2886–2892.[Notes]
7. Wang,W.; Pan, S.J.; Dahlmeier, D.; Xiao, X. Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, San Francisco, CA, USA, 4–9 February 2017; AAAI Press: San Francisco, CA, USA, 2017; pp. 3316–3322.
8. Xin Li, Lidong Bing, Piji Li, Wai Lam, and Zhimou Yang. 2018. Aspect term extraction with history attention and selective transformation. In IJCAI, pages 4194–4200
9. Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2019. An interactive multi-task learning network for end-to-end aspect-based sentiment analysis. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 504–515, Florence, Italy
10. Dehong Ma, Sujian Li, Fangzhao Wu, Xing Xie, and Houfeng Wang. 2019. Exploring sequence-to sequence learning in aspect term extraction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3538–3547.
11. Kun Li, Chengbo Chen, Xiaojun Quan, Qing Ling, Yan Song.2020. Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics,pages 7056-7066.

基于联合任务的抽取方法

基于联合任务的方法分为两大类，一类是联合target和opinion words，另一类是联合target和polarity的。

基于联合target and opinion words的方法

1. Wang, B., and Wang, H. 2008. Bootstrapping both product features and opinion words from chinese customer reviews with crossinducing. In IJCNLP ’08, 289–295.

2. Guang Qiu, Bing Liu, Jiajun Bu, and Chun Chen. 2011. Opinion Word Expansion and Target Extraction through Double Propagation. Computational Linguistics, 37(1):9–27.

3. Xinjie Zhou, Xiaojun Wan, and Jianguo Xiao. 2013. Collective opinion target extraction in Chinese microblogs. In EMNLP ’13, pages 1840–1850.

4. Wayne Xin Zhao, Jing Jiang, Hongfei Yan, and Xiaoming Li. Jointly modeling aspects and opinions with a maxent-lda hybrid. In EMNLP ’10, pages 56–65, 2010

5. Bishan Yang and Claire Cardie. 2013. Joint inference for fine-grained opinion extraction. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1640–1649.

6. Kang Liu, Liheng Xu, and Jun Zhao. 2014. Extracting opinion targets and opinion words from online reviews with graph co-ranking. In Proceedings of ACL, pages 314–324. Association for Computational Linguistics.

7. Qian Liu, Bing Liu, Yuanlin Zhang, Doo Soon Kim,and Zhiqiang Gao. 2016. Improving opinion aspect extraction using semantic similarity and aspect associations. In Thirtieth AAAI Conference on Artificial Intelligence.

8. Wenya Wang, Sinno Jialin Pan, and Daniel Dahlmeier. 2017b. Multi-task Memory Networks for Category-specific Aspect and Opinion Terms Co-extraction. arXiv preprint arXiv:1702.01776.

9. Qian Liu, Bing Liu, Yuanlin Zhang, Doo Soon Kim,and Zhiqiang Gao. 2016. Improving opinion aspect extraction using semantic similarity and aspect associations. In Thirtieth AAAI Conference on Artificial Intelligence.

基于联合target and polarity的方法

1. Zhiqiang Toh and Wenting Wang. 2014. DLIREC: Aspect term extraction and term polarity classification system. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), page 235.
2. Weichselbraun, A.; Gindl, S.; Fischer, F.; Vakulenko, S.; Scharl, A. Aspect-Based Extraction and Analysis of Affective Knowledge from Social Media Streams. IEEE Intell. Syst. 2017, 32, 80–88.
3. Li X , Bing L , Li P , et al. A Unified Model for Opinion Target Extraction and Target Sentiment Prediction. 2018.
4. Huaishao Luo, Tianrui Li, Bing Liu, and Junbo Zhang. 2019. DOER: Dual cross-shared RNN for aspect term-polarity co-extraction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 591–601, Florence, Italy

总结

1. 自情感分析任务存在，其语法、句法、评价对象的基本特性（距离，词性等等）从未被遗忘，而是伴随着机器学习，深度学习以及AI领域中一些新东西的出现带动着情感分析任务的发展，以此有了更多AI领域的新发现与情感分析的基本特性进行的结合而提出了不少新的思路和想法。
2. 情感分析可能会成为一种辅助性任务，如为推荐，阅读理解，问答等任务做铺垫，使得这些任务更具有人性化。
3. 总结的可能不够全面，会持续更新。。。

[1]（美）刘兵（BingLiu）著；刘康，赵军译. 情感分析 挖掘观点、情感和情绪. 北京：机械工业出版社, 2017.10.