Relevant bibliographies by topics / Minimally-supervised Learning

Academic literature on the topic 'Minimally-supervised Learning'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Minimally-supervised Learning"

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Althobaiti, Maha, Udo Kruschwitz, and Massimo Poesio. "Combining Minimally-supervised Methods for Arabic Named Entity Recognition." Transactions of the Association for Computational Linguistics 3 (December 2015): 243–55. http://dx.doi.org/10.1162/tacl_a_00136.

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Supervised methods can achieve high performance on NLP tasks, such as Named Entity Recognition (NER), but new annotations are required for every new domain and/or genre change. This has motivated research in minimally supervised methods such as semi-supervised learning and distant learning, but neither technique has yet achieved performance levels comparable to those of supervised methods. Semi-supervised methods tend to have very high precision but comparatively low recall, whereas distant learning tends to achieve higher recall but lower precision. This complementarity suggests that better results may be obtained by combining the two types of minimally supervised methods. In this paper we present a novel approach to Arabic NER using a combination of semi-supervised and distant learning techniques. We trained a semi-supervised NER classifier and another one using distant learning techniques, and then combined them using a variety of classifier combination schemes, including the Bayesian Classifier Combination (BCC) procedure recently proposed for sentiment analysis. According to our results, the BCC model leads to an increase in performance of 8 percentage points over the best base classifiers.
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Ruokolainen, Teemu, Oskar Kohonen, Kairit Sirts, Stig-Arne Grönroos, Mikko Kurimo, and Sami Virpioja. "A Comparative Study of Minimally Supervised Morphological Segmentation." Computational Linguistics 42, no. 1 (March 2016): 91–120. http://dx.doi.org/10.1162/coli_a_00243.

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This article presents a comparative study of a subfield of morphology learning referred to as minimally supervised morphological segmentation. In morphological segmentation, word forms are segmented into morphs, the surface forms of morphemes. In the minimally supervised data-driven learning setting, segmentation models are learned from a small number of manually annotated word forms and a large set of unannotated word forms. In addition to providing a literature survey on published methods, we present an in-depth empirical comparison on three diverse model families, including a detailed error analysis. Based on the literature survey, we conclude that the existing methodology contains substantial work on generative morph lexicon-based approaches and methods based on discriminative boundary detection. As for which approach has been more successful, both the previous work and the empirical evaluation presented here strongly imply that the current state of the art is yielded by the discriminative boundary detection methodology.
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Kiyomaru, Hirokazu, and Sadao Kurohashi. "Minimally-Supervised Joint Learning of Event Volitionality and Subject Animacy Classification." Journal of Natural Language Processing 29, no. 3 (2022): 807–34. http://dx.doi.org/10.5715/jnlp.29.807.

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Kiyomaru, Hirokazu, and Sadao Kurohashi. "Minimally-Supervised Joint Learning of Event Volitionality and Subject Animacy Classification." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 10921–29. http://dx.doi.org/10.1609/aaai.v36i10.21339.

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Volitionality and subject animacy are fundamental and closely related properties of an event. Their classification is challenging because it requires contextual text understanding and a huge amount of labeled data. This paper proposes a novel method that jointly learns volitionality and subject animacy at a low cost, heuristically labeling events in a raw corpus. Volitionality labels are assigned using a small lexicon of volitional and non-volitional adverbs such as deliberately and accidentally; subject animacy labels are assigned using a list of animate and inanimate nouns obtained from ontological knowledge. We then consider the problem of learning a classifier from the labeled events so that it can perform well on unlabeled events without the words used for labeling. We view the problem as a bias reduction or unsupervised domain adaptation problem and apply the techniques. We conduct experiments with crowdsourced gold data in Japanese and English and show that our method effectively learns volitionality and subject animacy without manually labeled data.
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Bi, Haixia, Feng Xu, Zhiqiang Wei, Yong Xue, and Zongben Xu. "An Active Deep Learning Approach for Minimally Supervised PolSAR Image Classification." IEEE Transactions on Geoscience and Remote Sensing 57, no. 11 (November 2019): 9378–95. http://dx.doi.org/10.1109/tgrs.2019.2926434.

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Moioli, Renan C., and Phil Husbands. "Neuronal Assembly Dynamics in Supervised and Unsupervised Learning Scenarios." Neural Computation 25, no. 11 (November 2013): 2934–75. http://dx.doi.org/10.1162/neco_a_00502.

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The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system's variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions.
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Ittoo, Ashwin, and Gosse Bouma. "Minimally-supervised learning of domain-specific causal relations using an open-domain corpus as knowledge base." Data & Knowledge Engineering 88 (November 2013): 142–63. http://dx.doi.org/10.1016/j.datak.2013.08.004.

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Curto, Sergio, Ana Cristina Mendes, and Luisa Coheur. "Question Generation based on Lexico-Syntactic Patterns Learned from the Web." Dialogue & Discourse 3, no. 2 (March 16, 2012): 147–75. http://dx.doi.org/10.5087/dad.2012.207.

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THE MENTOR automatically generates multiple-choice tests from a given text. This tool aims at supporting the dialogue system of the FalaComigo project, as one of FalaComigo's goals is the interaction with tourists through questions/answers and quizzes about their visit. In a minimally supervised learning process and by leveraging the redundancy and linguistic variability of the Web, THE MENTOR learns lexico-syntactic patterns using a set of question/answer seeds. Afterward, these patterns are used to match the sentences from which new questions (and answers) can be generated. Finally, several ï¬lters are applied in order to discard low quality items. In this paper we detail the question generation task as performed by T- Mand evaluate its performance.
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Zhang, Congle, Raphael Hoffmann, and Daniel Weld. "Ontological Smoothing for Relation Extraction with Minimal Supervision." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 157–63. http://dx.doi.org/10.1609/aaai.v26i1.8102.

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Relation extraction, the process of converting natural language text into structured knowledge, is increasingly important. Most successful techniques use supervised machine learning to generate extractors from sentences that have been manually labeled with the relations' arguments. Unfortunately, these methods require numerous training examples, which are expensive and time-consuming to produce. This paper presents ontological smoothing, a semi-supervisedtechnique that learns extractors for a set of minimally-labeledrelations. Ontological smoothing has three phases. First, itgenerates a mapping between the target relations and a backgroundknowledge-base. Second, it uses distant supervision toheuristically generate new training examples for the targetrelations. Finally, it learns an extractor from a combination of theoriginal and newly-generated examples. Experiments on 65 relationsacross three target domains show that ontological smoothing candramatically improve precision and recall, even rivaling fully supervisedperformance in many cases.
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Farr, Ryan J., Christina L. Rootes, John Stenos, Chwan Hong Foo, Christopher Cowled, and Cameron R. Stewart. "Detection of SARS-CoV-2 infection by microRNA profiling of the upper respiratory tract." PLOS ONE 17, no. 4 (April 5, 2022): e0265670. http://dx.doi.org/10.1371/journal.pone.0265670.

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Host biomarkers are increasingly being considered as tools for improved COVID-19 detection and prognosis. We recently profiled circulating host-encoded microRNA (miRNAs) during SARS-CoV-2 infection, revealing a signature that classified COVID-19 cases with 99.9% accuracy. Here we sought to develop a signature suited for clinical application by analyzing specimens collected using minimally invasive procedures. Eight miRNAs displayed altered expression in anterior nasal tissues from COVID-19 patients, with miR-142-3p, a negative regulator of interleukin-6 (IL-6) production, the most strongly upregulated. Supervised machine learning analysis revealed that a three-miRNA signature (miR-30c-2-3p, miR-628-3p and miR-93-5p) independently classifies COVID-19 cases with 100% accuracy. This study further defines the host miRNA response to SARS-CoV-2 infection and identifies candidate biomarkers for improved COVID-19 detection.
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Dissertations / Theses on the topic "Minimally-supervised Learning"

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Bai, Fan. "Structured Minimally Supervised Learning for Neural Relation Extraction." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu159666392917093.

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Wicentowski, Richard. "Modeling and learning multilingual inflectional morphology in a minimally supervised framework." Available to US Hopkins community, 2002. http://wwwlib.umi.com/dissertations/dlnow/3068229.

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Ali, Shahnewaz. "Robotic vision for knee arthroscopy." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/235890/1/Shahnewaz%2BAli%2BThesis%282%29.pdf.

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This research focuses on visualisation challenges associated with anatomical imaging of complex joints such as the human knee. Current imaging systems are inadequate to provide 3D perception and lack the level of situational awareness needed for performing highly complex minimally invasive surgeries like knee arthroscopy. As a result, unintended tissue damage is common occurrence and training new surgeons takes a very long time. To improve surgical precision and training, this study presents a series of novel methods and computational tools that provide 3D perception for safer surgery with added ability of automatically recognition of multiple tissue types in real time.
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Book chapters on the topic "Minimally-supervised Learning"

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Uszkoreit, Hans, Feiyu Xu, and Hong Li. "Analysis and Improvement of Minimally Supervised Machine Learning for Relation Extraction." In Natural Language Processing and Information Systems, 8–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12550-8_2.

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Conference papers on the topic "Minimally-supervised Learning"

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Ding, Kaize, Jundong Li, Nitesh Chawla, and Huan Liu. "Graph Minimally-supervised Learning." In WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3488560.3501390.

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Ding, Kaize, Chuxu Zhang, Jie Tang, Nitesh Chawla, and Huan Liu. "Toward Graph Minimally-Supervised Learning." In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3534678.3542602.

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Saito, Jun, Yugo Murawaki, and Sadao Kurohashi. "Minimally Supervised Learning of Affective Events Using Discourse Relations." In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-1581.

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Bi, Haixia, Feng Xu, Zhiqiang Wei, Yibo Han, Yuanlong Cui, Yong Xue, and Zongben Xu. "An Active Deep Learning Approach for Minimally-Supervised Polsar Image Classification." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8899214.

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Zhang, Xinyang, Chenwei Zhang, Xin Luna Dong, Jingbo Shang, and Jiawei Han. "Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks." In WWW '21: The Web Conference 2021. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3442381.3450114.

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Shao, Shuwei, Zhongcai Pei, Weihai Chen, Baochang Zhang, Xingming Wu, Dianmin Sun, and David Doermann. "Self-Supervised Learning for Monocular Depth Estimation on Minimally Invasive Surgery Scenes." In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. http://dx.doi.org/10.1109/icra48506.2021.9561508.

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Pascual, Damian, Amir Aminifar, and David Atienza. "A Self-Learning Methodology for Epileptic Seizure Detection with Minimally-Supervised Edge Labeling." In 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2019. http://dx.doi.org/10.23919/date.2019.8714995.

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Alkhatib, Wael, Leon Alexander Herrmann, and Christoph Rensing. "Onto.KOM - Towards a Minimally Supervised Ontology Learning System based on Word Embeddings and Convolutional Neural Networks." In 9th International Conference on Knowledge Engineering and Ontology Development. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006483000170026.

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Nguyen, Thao Pham Thanh, Takahiro Hayashi, Rikio Onai, Yuhei Nishioka, Takamasa Takenaka, and Masaya Mori. "A New Minimally Supervised Learning Method for Semantic Term Classification - Experimental Results on Classifying Ratable Aspects Discussed in Customer Reviews." In 2009 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2009. http://dx.doi.org/10.1109/icdmw.2009.58.

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Nguyen, Phuong, David Chapman, Sumeet Menon, Michael Morris, and Yelena Yesha. "Active semi-supervised expectation maximization learning for lung cancer detection from Computerized Tomography (CT) images with minimally label training data." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Maciej A. Mazurowski. SPIE, 2020. http://dx.doi.org/10.1117/12.2549655.

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