---
title: "Multiple Instance Learning Networks for Fine-Grained Sentiment"
description: "Document-level star ratings are plentiful, but labels for individual sentences or clauses are expensive… 54 references, cited by 28 papers here."
authors: ["Stefanos Angelidis", "Mirella Lapata"]
year: 2018
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/Q18-1002/"
doi: "10.1162/tacl_a_00002"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/angelidis-2018-multiple-instance-learning-networks-fine/"
---
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# Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis

**Authors:** [Stefanos Angelidis](https://md.research.elamur.ai/free/en/authors/stefanos-angelidis/) and [Mirella Lapata](https://md.research.elamur.ai/free/en/authors/mirella-lapata/) · **Year:** 2018 · **Venue:** [Transactions of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/tacl/) ([Transactions of the Association for Computational Linguistics (TACL) 2018](https://md.research.elamur.ai/free/en/venues/tacl/2018/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/Q18-1002/) · [PDF](https://aclanthology.org/Q18-1002.pdf) · [DOI 10.1162/tacl\_a\_00002](https://doi.org/10.1162/tacl_a_00002) · [OpenAlex](https://openalex.org/W2769436106)

**Citations:** 126 citations (OpenAlex, 2026-10-14) · [28](https://md.research.elamur.ai/free/en/papers/angelidis-2018-multiple-instance-learning-networks-fine/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Document-level star ratings are plentiful, but labels for individual sentences or clauses are expensive to collect. Yet a single review often mixes praise and complaints. Angelidis and Lapata treat segment-level polarity detection as a Multiple Instance Learning (MIL) problem: a review is a "bag" whose rating is known, while the polarities of its segments are hidden. Their model, MilNet, encodes each segment with a CNN and predicts sentiment for every segment. It then combines these predictions into a document label using attention weights, so it learns from document supervision alone. Segments can be sentences or EDUs (clause-like discourse units from Rhetorical Structure Theory). An attention-weighted polarity score is used to extract positive and negative snippets. To evaluate the approach, the authors built and released SpoT, a dataset with segment-level polarity labels. MilNet beat several baselines, including a hierarchical attention network (HierNet). Human judges also preferred its EDU-based opinion summaries. This shows that cheap review ratings can train fine-grained opinion extractors.

## Key points
- **SpoT dataset:** SpoT (Segment-level POlariTy annotations) labels both sentences and EDUs. It is released with the code at github.com/stangelid/milnet-sent.
- **Segmentation and encoder:** EDUs are obtained with the Feng and Hirst (2012) discourse parser. Segments are encoded with a Kim (2014)-style CNN, which costs less than 2% relative accuracy compared to GRU encoders on YELP'13 and IMDB document classification.
- **Comparison with HierNet:** HierNet builds a single document vector and assigns every segment one shared polarity. MilNet instead first scores each segment and then aggregates those scores up the hierarchy.
- **Human evaluation setup:** Four preference studies used all SpoT reviews, summaries at a 30% compression rate, and three judgments per document. Judges rated summaries on informativeness, polarity and coherence.
- **Human evaluation results:** With EDU segmentation, MilNet significantly beat HierNet on informativeness and polarity. MilNet's EDU summaries also beat its own sentence-based summaries and the LEAD and RANDOM baselines. HierNet was slightly favored on coherence, but this difference was not significant.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [University of Edinburgh](https://md.research.elamur.ai/free/en/universities/university-of-edinburgh/) — University, Edinburgh, United Kingdom · 359 works · 77,352 citations

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## Cite

Cite as: Stefanos Angelidis and Mirella Lapata. 2018. Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{angelidis2018multiple,
  title = {{Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis}},
  author = {Stefanos Angelidis and Mirella Lapata},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2018},
  doi = {10.1162/tacl\_a\_00002},
  url = {https://doi.org/10.1162/tacl_a_00002}
}
```

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## References

54 references, 23 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Stuart Andrews and Thomas Hofmann. 2004. Multiple instance learning via disjunctive programming boosting. In Advances in Neural Information Processing Systems 16, pages 65–72. [find](https://scholar.google.com/scholar?q=Stuart+Andrews+and+Thomas+Hofmann%2E+2004%2E+Multiple+instance+learning+via+disjunctive+programming+boosting%2E+In+Advances+in+Neural+Information+Processing+Systems+16%2C+pages+65%E2%80%9372%2E)
2. Curran Associates, Inc. Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. 2010. [SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining](https://md.research.elamur.ai/free/en/papers/baccianella-2010-sentiwordnet-3-0-enhanced-lexical/). In Proceedings of the 5th Conference on International Language Resources and Evaluation, volume 10, pages 2200–2204, Val…
3. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. [Neural machine translation by jointly learning to align and translate](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/). In Proceedings of the 3rd International Conference on Learning Representations, San Diego, California, USA.
4. Parminder Bhatia, Yangfeng Ji, and Jacob Eisenstein. 2015. [Better document-level sentiment analysis from RST discourse parsing](https://md.research.elamur.ai/free/en/papers/bhatia-2015-better-document-level-sentiment-analysis/). In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2212–2218, Lisbon, Portugal.
5. Ziqiang Cao, Wenjie Li, Sujian Li, and Furu Wei. 2017. Improving multi-document summarization via text classification. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, pages 3053–3058, San Francisco, California, USA. Peter Carbonetto, Gyuri Dorkó, Cordelia Schmid, Hendrik Küc… [find](https://scholar.google.com/scholar?q=Ziqiang+Cao%2C+Wenjie+Li%2C+Sujian+Li%2C+and+Furu+Wei%2E+2017%2E+Improving+multi%2Ddocument+summarization+via+text+classification%2E+In+Proceedings+of+the+31st+AAAI+Conference+on+Artificial+Intelligence%2C+pages+3053)
6. Giuseppe Carenini, Rymond Ng, and Adam Pauls. 2006. Multidocument summarization of evaluative text. In Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics, pages 305–312, Trento, Italy. [find](https://scholar.google.com/scholar?q=Giuseppe+Carenini%2C+Rymond+Ng%2C+and+Adam+Pauls%2E+2006%2E+Multidocument+summarization+of+evaluative+text%2E+In+Proceedings+of+the+11th+Conference+of+the+European+Chapter+of+the+Association+for+Computational+L)
7. Lynn Carlson, Daniel Marcu, and Mary Ellen Okurowski. 2003. [Building a discourse-tagged corpus in the framework of rhetorical structure theory](https://md.research.elamur.ai/free/en/papers/carlson-2001-building-discourse-tagged-corpus-framework/). In Current and New Directions in Discourse and Dialogue, pages 85–112. Springer.
8. Jianpeng Cheng and Mirella Lapata. 2016. [Neural summarization by extracting sentences and words](https://md.research.elamur.ai/free/en/papers/cheng-2016-neural-summarization-extracting-sentences-words/). In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 484–494, Berlin, Germany.
9. Timothee Cour, Ben Sapp, and Ben Taskar. 2011. Learning from partial labels. Journal of Machine Learning Research, 12(May):1501–1536. Hal Daumé III and Daniel Marcu. 2002. A noisy-channel model for document compression. In Proceedings of the 40th Annual Meeting of the Association for Computational… [find](https://scholar.google.com/scholar?q=Timothee+Cour%2C+Ben+Sapp%2C+and+Ben+Taskar%2E+2011%2E+Learning+from+partial+labels%2E+Journal+of+Machine+Learning+Research%2C+12%28May%29%3A1501%E2%80%931536%2E+Hal+Daume%CC%81+III+and+Daniel+Marcu%2E+2002%2E+A+noisy%2Dchannel+model+for+d)
10. Misha Denil, Alban Demiraj, and Nando de Freitas. 2014. Extraction of salient sentences from labelled documents. Technical report, University of Oxford. [find](https://scholar.google.com/scholar?q=Misha+Denil%2C+Alban+Demiraj%2C+and+Nando+de+Freitas%2E+2014%2E+Extraction+of+salient+sentences+from+labelled+documents%2E+Technical+report%2C+University+of+Oxford%2E)
11. Giuseppe Di Fabbrizio, Amanda Stent, and Robert Gaizauskas. 2014. A hybrid approach to multi-document summarization of opinions in reviews. In Proceedings of the 8th International Natural Language Generation Conference (INLG), pages 54–63, Philadelphia, Pennsylvania, USA. [find](https://scholar.google.com/scholar?q=Giuseppe+Di+Fabbrizio%2C+Amanda+Stent%2C+and+Robert+Gaizauskas%2E+2014%2E+A+hybrid+approach+to+multi%2Ddocument+summarization+of+opinions+in+reviews%2E+In+Proceedings+of+the+8th+International+Natural+Language+Gen)
12. Qiming Diao, Minghui Qiu, Chao-Yuan Wu, Alexander J. Smola, Jing Jiang, and Chong Wang. 2014. Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS). In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 193– 202, New… [find](https://scholar.google.com/scholar?q=Qiming+Diao%2C+Minghui+Qiu%2C+Chao%2DYuan+Wu%2C+Alexander+J%2E+Smola%2C+Jing+Jiang%2C+and+Chong+Wang%2E+2014%2E+Jointly+modeling+aspects%2C+ratings+and+sentiments+for+movie+recommendation+%28JMARS%29%2E+In+Proceedings+of+the+2)
13. Thomas G. Dietterich, Richard H. Lathrop, and Toms Lozano-Prez. 1997. Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence, 89(1):31 – 71. [find](https://scholar.google.com/scholar?q=Thomas+G%2E+Dietterich%2C+Richard+H%2E+Lathrop%2C+and+Toms+Lozano%2DPrez%2E+1997%2E+Solving+the+multiple+instance+problem+with+axis%2Dparallel+rectangles%2E+Artificial+Intelligence%2C+89%281%29%3A31+%E2%80%93+71%2E)
14. Wei Vanessa Feng and Graeme Hirst. 2012. [Text-level discourse parsing with rich linguistic features](https://md.research.elamur.ai/free/en/papers/feng-2012-text-level-discourse-parsing-rich/). In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 60–68, Jeju Island, Korea.
15. Kavita Ganesan, ChengXiang Zhai, and Jiawei Han. 2010. [Opinosis: A graph based approach to abstractive summarization of highly redundant opinions](https://md.research.elamur.ai/free/en/papers/ganesan-2010-opinosis-graph-based-approach-abstractive/). In Proceedings of the 23rd International Conference on Computational Linguistics, pages 340–348, Beijing, China.
16. Shima Gerani, Yashar Mehdad, Giuseppe Carenini, Raymond T. Ng, and Bita Nejat. 2014. [Abstractive summarization of product reviews using discourse structure](https://md.research.elamur.ai/free/en/papers/gerani-2014-abstractive-summarization-product-reviews-discourse/). In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pages 1602–1613, Doha, Qatar.
17. Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer, and Daniel S Weld. 2011. Knowledge-based weak supervision for information extraction of overlapping relations. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1… [find](https://scholar.google.com/scholar?q=Raphael+Hoffmann%2C+Congle+Zhang%2C+Xiao+Ling%2C+Luke+Zettlemoyer%2C+and+Daniel+S+Weld%2E+2011%2E+Knowledge%2Dbased+weak+supervision+for+information+extraction+of+overlapping+relations%2E+In+Proceedings+of+the+49th+A)
18. Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 168–177, Seattle, Washington, USA. [find](https://scholar.google.com/scholar?q=Minqing+Hu+and+Bing+Liu%2E+2004%2E+Mining+and+summarizing+customer+reviews%2E+In+Proceedings+of+the+10th+ACM+SIGKDD+International+Conference+on+Knowledge+Discovery+and+Data+Mining%2C+pages+168%E2%80%93177%2C+Seattle%2C+W)
19. Rie Johnson and Tong Zhang. 2015a. [Effective use of word order for text categorization with convolutional neural networks](https://md.research.elamur.ai/free/en/papers/johnson-2015-effective-use-word-order-text/). In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 103–112, Denver, Colorad…
20. Rie Johnson and Tong Zhang. 2015b. Semi-supervised convolutional neural networks for text categorization via region embedding. In Advances in Neural Information Processing Systems 28, pages 919–927. [find](https://scholar.google.com/scholar?q=Rie+Johnson+and+Tong+Zhang%2E+2015b%2E+Semi%2Dsupervised+convolutional+neural+networks+for+text+categorization+via+region+embedding%2E+In+Advances+in+Neural+Information+Processing+Systems+28%2C+pages+919%E2%80%93927%2E)
21. Curran Associates, Inc. Jim Keeler and David E. Rumelhart. 1992. A self-organizing integrated segmentation and recognition neural net. In Advances in Neural Information Processing Systems 4, pages 496–503. [find](https://scholar.google.com/scholar?q=Curran+Associates%2C+Inc%2E+Jim+Keeler+and+David+E%2E+Rumelhart%2E+1992%2E+A+self%2Dorganizing+integrated+segmentation+and+recognition+neural+net%2E+In+Advances+in+Neural+Information+Processing+Systems+4%2C+pages+496)
22. Morgan-Kaufmann. Yoon Kim. 2014. [Convolutional neural networks for sentence classification](https://md.research.elamur.ai/free/en/papers/kim-2014-convolutional-neural-networks-sentence-classification/). In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pages 1746–1751, Doha, Qatar.
23. Dimitrios Kotzias, Misha Denil, Nando De Freitas, and Padhraic Smyth. 2015. From group to individual labels using deep features. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 597–606, Sydney, Australia. [find](https://scholar.google.com/scholar?q=Dimitrios+Kotzias%2C+Misha+Denil%2C+Nando+De+Freitas%2C+and+Padhraic+Smyth%2E+2015%2E+From+group+to+individual+labels+using+deep+features%2E+In+Proceedings+of+the+21th+ACM+SIGKDD+International+Conference+on+Knowl)
24. Quoc Le and Tomas Mikolov. 2014. [Distributed representations of sentences and documents](https://md.research.elamur.ai/free/en/papers/le-2014-distributed-representations-sentences-documents/). In Proceedings of the 31st International Conference on Machine Learning, pages 1188–1196, Beijing, China.
25. Kevin Lerman, Sasha Blair-Goldensohn, and Ryan Mc-Donald. 2009. Sentiment summarization: Evaluating and learning user preferences. In Proceedings of the 12th Conference of the European Chapter of the ACL, pages 514–522, Athens, Greece. [find](https://scholar.google.com/scholar?q=Kevin+Lerman%2C+Sasha+Blair%2DGoldensohn%2C+and+Ryan+Mc%2DDonald%2E+2009%2E+Sentiment+summarization%3A+Evaluating+and+learning+user+preferences%2E+In+Proceedings+of+the+12th+Conference+of+the+European+Chapter+of+the+)
26. Junyi Jessy Li, Kapil Thadani, and Amanda Stent. 2016. The role of discourse units in near-extractive summarization. In Proceedings of the SIGDIAL 2016 Conference, The 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 137–147, Los Angeles, California, USA. [find](https://scholar.google.com/scholar?q=Junyi+Jessy+Li%2C+Kapil+Thadani%2C+and+Amanda+Stent%2E+2016%2E+The+role+of+discourse+units+in+near%2Dextractive+summarization%2E+In+Proceedings+of+the+SIGDIAL+2016+Conference%2C+The+17th+Annual+Meeting+of+the+Speci)
27. William C. Mann and Sandra A. Thompson. 1988. [Rhetorical structure theory: Toward a functional theory of text organization](https://md.research.elamur.ai/free/en/papers/mann-1988-rhetorical-structure-theory-functional/). Text-Interdisciplinary Journal for the Study of Discourse, 8(3):243–281.
28. Oded Maron and Aparna Lakshmi Ratan. 1998. Multiple-instance learning for natural scene classification. In Proceedings of the 15th International Conference on Machine Learning, volume 98, pages 341– 349, San Francisco, California, USA. [find](https://scholar.google.com/scholar?q=Oded+Maron+and+Aparna+Lakshmi+Ratan%2E+1998%2E+Multiple%2Dinstance+learning+for+natural+scene+classification%2E+In+Proceedings+of+the+15th+International+Conference+on+Machine+Learning%2C+volume+98%2C+pages+341%E2%80%93+3)
29. Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 2017. [SummaRuNNer: A recurrent neural network based sequence model for extractive summarization of documents](https://md.research.elamur.ai/free/en/papers/nallapati-2016-summarunner-recurrent-neural-network-based/). In Proceedings of the 31st AAAI Conference on Artificial Intelligence, pages 3075–3081, San Francisco, California.
30. Ani Nenkova. 2005. Automatic text summarization of newswire: Lessons learned from the document understanding conference. In Proceedings of the 20th AAAI, pages 1436–1441, Pittsburgh, Pennsylvania, USA. [find](https://scholar.google.com/scholar?q=Ani+Nenkova%2E+2005%2E+Automatic+text+summarization+of+newswire%3A+Lessons+learned+from+the+document+understanding+conference%2E+In+Proceedings+of+the+20th+AAAI%2C+pages+1436%E2%80%931441%2C+Pittsburgh%2C+Pennsylvania%2C+USA)
31. Eric Noreen. 1989. Computer-intensive Methods for Testing Hypotheses: An Introduction. [find](https://scholar.google.com/scholar?q=Eric+Noreen%2E+1989%2E+Computer%2Dintensive+Methods+for+Testing+Hypotheses%3A+An+Introduction%2E)
32. Wiley. Bo Pang and Lillian Lee. 2005. [Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales](https://md.research.elamur.ai/free/en/papers/pang-2005-seeing-stars-exploiting-class-relationships/). In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pages 115–124. Association for Computational Linguistics.
33. Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002. [Thumbs up? sentiment classification using machine learning techniques](https://md.research.elamur.ai/free/en/papers/pang-2002-thumbs-up-sentiment-classification-machine/). In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, pages 79–86, Pittsburgh, Pennsylvania, USA.
34. Nikolaos Pappas and Andrei Popescu-Belis. 2014. Explaining the stars: Weighted multiple-instance learning for aspect-based sentiment analysis. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pages 455–466, Doha, Qatar, October. [find](https://scholar.google.com/scholar?q=Nikolaos+Pappas+and+Andrei+Popescu%2DBelis%2E+2014%2E+Explaining+the+stars%3A+Weighted+multiple%2Dinstance+learning+for+aspect%2Dbased+sentiment+analysis%2E+In+Proceedings+of+the+2014+Conference+on+Empirical+Method)
35. Nikolaos Pappas and Andrei Popescu-Belis. 2017. Explicit document modeling through weighted multiple-instance learning. Journal of Artificial Intelligence Research, 58:591–626. [find](https://scholar.google.com/scholar?q=Nikolaos+Pappas+and+Andrei+Popescu%2DBelis%2E+2017%2E+Explicit+document+modeling+through+weighted+multiple%2Dinstance+learning%2E+Journal+of+Artificial+Intelligence+Research%2C+58%3A591%E2%80%93626%2E)
36. Lizhen Qu, Georgiana Ifrim, and Gerhard Weikum. 2010. The bag-of-opinions method for review rating prediction from sparse text patterns. In Proceedings of the 23rd International Conference on Computational Linguistics, pages 913–921, Beijing, China. [find](https://scholar.google.com/scholar?q=Lizhen+Qu%2C+Georgiana+Ifrim%2C+and+Gerhard+Weikum%2E+2010%2E+The+bag%2Dof%2Dopinions+method+for+review+rating+prediction+from+sparse+text+patterns%2E+In+Proceedings+of+the+23rd+International+Conference+on+Computat)
37. Richard Socher, Jeffrey Pennington, Eric H. Huang, Andrew Y. Ng, and Christopher D. Manning. 2011. [Semi-supervised recursive autoencoders for predicting sentiment distributions](https://md.research.elamur.ai/free/en/papers/socher-2011-semi-supervised-recursive-autoencoders-predicting/). In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 151–161, Edinburgh, Scotl…
38. Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. 2013. [Recursive deep models for semantic compositionality over a sentiment treebank](https://md.research.elamur.ai/free/en/papers/socher-2013-recursive-deep-models-semantic-compositionality/). In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 16…
39. Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll, and Manfred Stede. 2011. [Lexicon-based methods for sentiment analysis](https://md.research.elamur.ai/free/en/papers/taboada-2011-lexicon-based-methods-sentiment-analysis/). Computational Linguistics, 37(2):267–307. Oscar Täckström and Ryan McDonald. 2011. Discovering fine-grained sentiment with latent variable structured prediction mode…
40. Duyu Tang, Bing Qin, and Ting Liu. 2015. [Document modeling with gated recurrent neural network for sentiment classification](https://md.research.elamur.ai/free/en/papers/tang-2015-document-modeling-gated-recurrent-neural/). In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1422–1432, Lisbon, Portugal.
41. Peter D Turney. 2002. [Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews](https://md.research.elamur.ai/free/en/papers/turney-2002-thumbs-up-or-thumbs-down/). In Proceedings of the 40th annual meeting on association for computational linguistics, pages 417–424, Pittsburgh, Pennsylvania, USA.
42. Sida Wang and Christopher D. Manning. 2012. [Baselines and bigrams: Simple, good sentiment and topic classification](https://md.research.elamur.ai/free/en/papers/wang-2012-baselines-bigrams-simple-good-sentiment/). In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2, pages 90–94, Jeju Island, Korea.
43. Xiu-Shen Wei, Jianxin Wu, and Zhi-Hua Zhou. 2014. Scalable multi-instance learning. In Proceedings of the IEEE International Conference on Data Mining, pages 1037–1042, Shenzhen, China. [find](https://scholar.google.com/scholar?q=Xiu%2DShen+Wei%2C+Jianxin+Wu%2C+and+Zhi%2DHua+Zhou%2E+2014%2E+Scalable+multi%2Dinstance+learning%2E+In+Proceedings+of+the+IEEE+International+Conference+on+Data+Mining%2C+pages+1037%E2%80%931042%2C+Shenzhen%2C+China%2E)
44. Nils Weidmann, Eibe Frank, and Bernhard Pfahringer. 2003. A two-level learning method for generalized multi-instance problems. In Proceedings of the 14th European Conference on Machine Learning, pages 468–479, Dubrovnik, Croatia. [find](https://scholar.google.com/scholar?q=Nils+Weidmann%2C+Eibe+Frank%2C+and+Bernhard+Pfahringer%2E+2003%2E+A+two%2Dlevel+learning+method+for+generalized+multi%2Dinstance+problems%2E+In+Proceedings+of+the+14th+European+Conference+on+Machine+Learning%2C+pages)
45. Janyce Wiebe, Theresa Wilson, and Claire Cardie. 2005. Annotating expressions of opinions and emotions in language. Language resources and evaluation, 39(2):165–210. [find](https://scholar.google.com/scholar?q=Janyce+Wiebe%2C+Theresa+Wilson%2C+and+Claire+Cardie%2E+2005%2E+Annotating+expressions+of+opinions+and+emotions+in+language%2E+Language+resources+and+evaluation%2C+39%282%29%3A165%E2%80%93210%2E)
46. Jiajun Wu, Yinan Yu, Chang Huang, and Kai Yu. 2015. Deep multiple instance learning for image classification and auto-annotation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3460–3469, Boston, Massachusetts, USA. [find](https://scholar.google.com/scholar?q=Jiajun+Wu%2C+Yinan+Yu%2C+Chang+Huang%2C+and+Kai+Yu%2E+2015%2E+Deep+multiple+instance+learning+for+image+classification+and+auto%2Dannotation%2E+In+Proceedings+of+the+IEEE+Conference+on+Computer+Vision+and+Pattern+R)
47. Rui Xia and Chengqing Zong. 2010. Exploring the use of word relation features for sentiment classification. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pages 1336– 1344, Beijing, China. [find](https://scholar.google.com/scholar?q=Rui+Xia+and+Chengqing+Zong%2E+2010%2E+Exploring+the+use+of+word+relation+features+for+sentiment+classification%2E+In+Proceedings+of+the+23rd+International+Conference+on+Computational+Linguistics%3A+Posters%2C+p)
48. Xin Xu and Eibe Frank. 2004. Logistic regression and boosting for labeled bags of instances. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 272–281. [find](https://scholar.google.com/scholar?q=Xin+Xu+and+Eibe+Frank%2E+2004%2E+Logistic+regression+and+boosting+for+labeled+bags+of+instances%2E+In+Proceedings+of+the+Pacific%2DAsia+Conference+on+Knowledge+Discovery+and+Data+Mining%2C+pages+272%E2%80%93281%2E)
49. Springer-Verlag. Wei Xu, Alan Ritter, Chris Callison-Burch, William B. Dolan, and Yangfeng Ji. 2014. Extracting lexically divergent paraphrases from Twitter. Transactions of the Association for Computational Linguistics, 2:435– 448. [find](https://scholar.google.com/scholar?q=Springer%2DVerlag%2E+Wei+Xu%2C+Alan+Ritter%2C+Chris+Callison%2DBurch%2C+William+B%2E+Dolan%2C+and+Yangfeng+Ji%2E+2014%2E+Extracting+lexically+divergent+paraphrases+from+Twitter%2E+Transactions+of+the+Association+for+Comput)
50. Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. [Hierarchical attention networks for document classification](https://md.research.elamur.ai/free/en/papers/yang-2016-hierarchical-attention-networks-document-classification/). In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 14…
51. Matthew D. Zeiler. 2012. [ADADELTA: an adaptive learning rate method](https://md.research.elamur.ai/free/en/papers/zeiler-2012-adadelta-adaptive-learning-rate-method/). CoRR, abs/1212.5701.
52. Qi Zhang, Sally A. Goldman, Wei Yu, and Jason E. Fritts. 2002. Content-based image retrieval using multiple-instance learning. In Proceedings of the 19th International Conference on Machine Learning, volume 2, pages 682–689, Sydney, Australia. [find](https://scholar.google.com/scholar?q=Qi+Zhang%2C+Sally+A%2E+Goldman%2C+Wei+Yu%2C+and+Jason+E%2E+Fritts%2E+2002%2E+Content%2Dbased+image+retrieval+using+multiple%2Dinstance+learning%2E+In+Proceedings+of+the+19th+International+Conference+on+Machine+Learning%2C+)
53. Cha Zhang, John C. Platt, and Paul A. Viola. 2006. Multiple instance boosting for object detection. In Advances in Neural Information Processing Systems 18, pages 1417–1424. [find](https://scholar.google.com/scholar?q=Cha+Zhang%2C+John+C%2E+Platt%2C+and+Paul+A%2E+Viola%2E+2006%2E+Multiple+instance+boosting+for+object+detection%2E+In+Advances+in+Neural+Information+Processing+Systems+18%2C+pages+1417%E2%80%931424%2E)
54. MIT Press. Zhi-Hua Zhou, Yu-Yin Sun, and Yu-Feng Li. 2009. Multi-instance learning by treating instances as non-iid samples. In Proceedings of the 26th Annual International Conference on Machine Learning, pages 1249–1256, Montréal, Quebec. [find](https://scholar.google.com/scholar?q=MIT+Press%2E+Zhi%2DHua+Zhou%2C+Yu%2DYin+Sun%2C+and+Yu%2DFeng+Li%2E+2009%2E+Multi%2Dinstance+learning+by+treating+instances+as+non%2Diid+samples%2E+In+Proceedings+of+the+26th+Annual+International+Conference+on+Machine+Learn)

*Updated 29 October 2026*