136041 SE Weakly Supervised Machine Learning (2021S)
- Registration is open from Mo 01.02.2021 09:00 to Th 25.02.2021 23:59
- Deregistration possible until We 31.03.2021 23:59
Classes (iCal) - next class is marked with N
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Aims, contents and method of the course
Assessment and permitted materials
Minimum requirements and assessment criteria
Craven et al. (1999). Constructing biological knowledge bases by extracting information from text sources.Mintz et al. (2009). Distant supervision for relation extraction without labeled data.Riedel et al. (2010). Modeling relations and their mentions without labeled text.Surdeanu et al. (2012). Multi-instance multi-label learning for relation extraction.Ritter et al. (2013). Modeling missing data in distant supervision for information extraction.*Universal Schemas*
Riedel et al. (2013). Relation extraction with matrix factorization and universal schemas.Verga et al. (2015). Multilingual relation extraction using compositional universal schema.*Label Denoising*
Griffiths et al. (2004). Finding scientific topics.
Alfonseca, E., Filippova, K., Delort, J. Y., & Garrido, G. (2012). Pattern Learning for Relation Extraction with Hierarchical Topic Models.Lin et al. (2016). Neural relation extraction with selective attention over instances.Luo et al. (2017). Learning with noise: Enhance distantly supervised relation extraction with dynamic transition matrix."Cleanlab"
Northcutt et al. (2019). Confident learning: Estimating uncertainty in dataset labels.
Stephen et al. (2017). Learning the Structure of Generative Models without Labeled Data.
https://www.snorkel.org/Feng et al. (2018). Reinforcement learning for relation classification from noisy data.Dehghani et al. (2017). Fidelity-weighted learning.Zheng et al. (2019). Meta label correction for learning with weak supervision.Awasthi et al. (2020). Learning from rules generalizing labeled exemplars.*Adversarial methods*
Miyato et al. (2016). Adversarial training methods for semi-supervised text classification.
Wu et al. (2017). Adversarial training for relation extraction.Zeng et al. (2018). Adversarial learning for distant supervised relation extraction.Qin et al. (2018). Dsgan: Generative adversarial training for distant supervision relation extraction.* Applications *"Named Entity Recognition"
Rehbein et al. (2017). Detecting annotation noise in automatically labelled data.
Hovy et al. (2013). Learning whom to trust with MACE.Lison et al. (2020). Named entity recognition without labelled data: A weak supervision approach.Liang et al. (2020). Bond: Bert-assisted open-domain named entity recognition with distant supervision."Low-resource Named Entity Recognition"
Hedderich et al. (2020). Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages.
Hedderich and Klakow (2018). Training a neural network in a low-resource settingon automatically annotated noisy data.
Lv et al. (2020). Matrix smoothing: A regularization for DNN with transition matrix under noisy labels.Cao et al. (2020). Unsupervised Parsing via Constituency Tests."Sentiment"
Turney (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews.
Go et al. (2009). Twitter sentiment classification using distant supervision.
Hogenboom et al. (2013). Exploiting emoticons in sentiment analysis.Guo et al. (2011). A weakly-supervised approach to argumentative zoning of scientific documents.Rutherford et al. (2015). Improving the inference of implicit discourse relations via classifying explicit discourse connectives.Wallace et al. (2016). Extracting PICO sentences from clinical trial reports using supervised distant supervision.Keith et al. (2017). Identifying civilians killed by police with distantly supervised entity-event extraction.Li et al. (2014). Weakly supervised user profile extraction from twitter.Lee et al. (2019). Latent retrieval for weakly supervised open domain question answering.Hashimoto (2019). Weakly supervised multilingual causality extraction from wikipedia.