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Representation Learning and NLP
[chapter]
2020
Representation Learning for Natural Language Processing
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This chapter presents a brief introduction to representation learning, including its motivation and basic idea, and also reviews its history and recent advances in both machine learning and NLP. ...
Representation learning aims to learn representations of raw data as useful information for further classification or prediction. ...
A typical machine learning system consists of three components [5] : Machine Learning = Representation + Objective + Optimization. (1.1) That is, to build an effective machine learning system, we first ...
doi:10.1007/978-981-15-5573-2_1
fatcat:4vahgg7ehffytb3qcvfgmo7wsm
Learning joint representation for community question answering with tri-modal DBM
2014
Proceedings of the 23rd International Conference on World Wide Web - WWW '14 Companion
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In light of these issues, we proposed a trimodal deep boltzmann machine (tri-DBM) to extract unified representation for query, question and answer. Experiments on Yahoo! ...
Answers dataset reveal using these unified representation to train a classifier judging semantic matching level between query and question outperforms models using bag-of-words or LSA representation significantly ...
Its energy function of state v and h is computed as follows: (1) where θ = (W, b, c) are model parameters to be learned and M is total number of words occurred in a query or question. ...
doi:10.1145/2567948.2577341
dblp:conf/www/PengROLX14
fatcat:hzimjtp6qrfavmobn7feconszi
Toward a unified science of machine learning
1989
Machine Learning
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Such integrated approaches provide a good role model for the rest of machine learning. ...
EDITORIAL Toward a Unified Science of Machine Learning Diversification and unification Machine learning is a diverse discipline that acts as host to a variety of research goals, learning techniques, and ...
doi:10.1007/bf00116834
fatcat:xlob4qoyffa6dpmerskxvqea5m
Combine Factual Medical Knowledge and Distributed Word Representation to Improve Clinical Named Entity Recognition
2018
AMIA Annual Symposium Proceedings
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The evaluation results showed that the RNN with medical knowledge as embedding layers achieved new state-of-the-art performance (a strict F1 score of 86.21% and a relaxed F1 score of 92.80%) on the 2010 ...
However, it is still not clear how existing medical knowledge can help deep learning models in clinical NER tasks. ...
We would like to thank the 2010 i2b2/VA challenge organizers for the development of the corpus used in this study. ...
pmid:30815153
pmcid:PMC6371322
fatcat:tl6cz77hnbeyzhj6miyjs7qxu4
Intelligent Interface for Knowledge Based System
2014
TELKOMNIKA (Telecommunication Computing Electronics and Control)
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One of the solutions to overcoming this problem is providing a unified model that can accept all types of knowledge, which guarantees automatic interaction between the knowledge-based systems. ...
It will help to acceleratethe establishment of a new knowledge-based system because it does not need knowledge initialization. ...
From the proposed solution point of view, the model of knowledge can also be approached using machine learning and the expert system along with an appropriate method of reasoning. ...
doi:10.12928/telkomnika.v12i4.413
fatcat:lxjbkvwfvrg6nfxeyc75jwutey
Go From the General to the Particular: Multi-Domain Translation with Domain Transformation Networks
[article]
2019
arXiv
pre-print
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The key challenge of multi-domain translation lies in simultaneously encoding both the general knowledge shared across domains and the particular knowledge distinctive to each domain in a unified model ...
To guarantee the knowledge transformation, we also propose two complementary supervision signals by leveraging the power of knowledge distillation and adversarial learning. ...
Towards learning a unified multi-domain translation model, several researchers turn to augment the NMT model to learn domain-specific knowledge. ...
arXiv:1911.09912v1
fatcat:yofseyxf5ngjjpuhp5ndhuwb6y
Go From the General to the Particular: Multi-Domain Translation with Domain Transformation Networks
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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The key challenge of multi-domain translation lies in simultaneously encoding both the general knowledge shared across domains and the particular knowledge distinctive to each domain in a unified model ...
To guarantee the knowledge transformation, we also propose two complementary supervision signals by leveraging the power of knowledge distillation and adversarial learning. ...
Towards learning a unified multi-domain translation model, several researchers turn to augment the NMT model to learn domain-specific knowledge. ...
doi:10.1609/aaai.v34i05.6461
fatcat:7jjzoft3yne43mxox66veyw5oe
Sliced Cramer Synaptic Consolidation for Preserving Deeply Learned Representations
2020
International Conference on Learning Representations
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We then propose a fundamentally different class of preservation methods that aim at preserving the distribution of the network's output at an arbitrary layer for previous tasks while learning a new one ...
We explore such selective synaptic plasticity approaches through a unifying lens of memory replay and show the close relationship between methods like Elastic Weight Consolidation (EWC) and Memory-Aware-Synapses ...
INTRODUCTION Incremental learning without catastrophic forgetting is one of the core characteristics of a lifelong learning machine (L2M) and has recently gained renewed attention from the machine learning ...
dblp:conf/iclr/KolouriKSP20
fatcat:ku3hiiyenfdr5c52f7wp7sixpq
Managing Machine Learning Workflow Components
[article]
2020
arXiv
pre-print
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To handle this problem, in this paper, we introduce machine learning workflow management (MLWfM) as a technique to aid the development and reuse of MLWfs and their components through three aspects: representation ...
Also, we consider the execution of these components within a tool. The hybrid knowledge representation, called Hyperknowledge, frames our methodology, supporting the three MLWfM's aspects. ...
Representation of Machine Learning Workflows In this section, we describe the knowledge model elements that support our MLWfM method. ...
arXiv:1912.05665v2
fatcat:lvw6urp2tvbk3nu2zopn4ipqii
A Unified View of Relational Deep Learning for Drug Pair Scoring
[article]
2021
arXiv
pre-print
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Here, we present a unified theoretical view of relational machine learning models which can address these tasks. ...
In recent years, numerous machine learning models which attempt to solve polypharmacy side effect identification, drug-drug interaction prediction and combination therapy design tasks have been proposed ...
Stephen Bonner is a fellow of the AstraZeneca postdoctoral program. ...
arXiv:2111.02916v4
fatcat:wyzysblwqfefdemmk2dhwtlvxa
Knowledge Representation and Management: Interest in New Solutions for Ontology Curation
2021
IMIA Yearbook of Medical Informatics
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Knowledge representations are key to advance machine learning by providing context and to develop novel bioinformatics metrics. ...
Objective: To select, present and summarize some of the best papers in the field of Knowledge Representation and Management (KRM) published in 2020. ...
in the selection process of the KRM best papers. ...
doi:10.1055/s-0041-1726508
pmid:34479390
fatcat:qkgdqbj4irhcrihnhms362igkm
Propositionalization and embeddings: two sides of the same coin
2020
Machine Learning
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Data preprocessing is an important component of machine learning pipelines, which requires ample time and resources. ...
This paper outlines some of the modern data processing techniques used in relational learning that enable data fusion from different input data types and formats into a single table data representation ...
One of the main strategic problems machine learning has to solve is better integration of knowledge and models across different domains and representations. ...
doi:10.1007/s10994-020-05890-8
pmid:32704202
pmcid:PMC7366599
fatcat:byyvqrplkrdvbcqvfctswm3ncu
Propositionalization and Embeddings: Two Sides of the Same Coin
[article]
2020
arXiv
pre-print
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Data preprocessing is an important component of machine learning pipelines, which requires ample time and resources. ...
This paper outlines some of the modern data processing techniques used in relational learning that enable data fusion from different input data types and formats into a single table data representation ...
The work of the second author was funded by the Slovenian Research Agency through a young researcher grant. ...
arXiv:2006.04410v1
fatcat:idpgnam52jdnbbpv32qhm7o3im
Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes
2020
BMC Medical Informatics and Decision Making
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Results The best configuration of the employed machine learning models yielded a competitive AUC of 0.97. ...
Methods We conducted a secondary analysis of Medical Information Mart for Intensive Care III (MIMIC-III) data. Different machine learning modeling and NLP approaches were applied. ...
Acknowledgements
Not applicable
About this supplement This article has been published as part of BMC Medical Informatics and Decision Making Volume 20 Supplement 11 2020: Informatics and machine learning ...
doi:10.1186/s12911-020-01318-4
pmid:33380338
fatcat:mljzj3vorrbezm2vtz6s2nanae
MolRep: A Deep Representation Learning Library for Molecular Property Prediction
[article]
2021
bioRxiv
pre-print
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Herein, we have developed MolRep by unifying 16 state-of-the-art models across 4 popular molecular representations for application and comparison. ...
However, unified frameworks have not yet emerged for fairly measuring algorithmic progress, and experimental procedures of different representation models often lack rigorousness and are hardly reproducible ...
., 2019a) , they either contained a few deep representation learning models or require many efforts to perform unified evaluation process and hyper-parameter searching. ...
doi:10.1101/2021.01.13.426489
fatcat:vnjctlklx5hs5ehr6egvnsxigi
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