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Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing
[article]
2018
arXiv
pre-print
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Knowledge bases (KBs) are paramount in NLP. We employ multiview learning for increasing accuracy and coverage of entity type information in KBs. We rely on two metaviews: language and representation. ...
For representation, we consider representations based on the context distribution of the entity (i.e., on its embedding), on the entity's name (i.e., on its surface form) and on its description in Wikipedia ...
Hazen and the anonymous reviewers for their helpful feedback on earlier drafts. This work was partially supported by the European Research Council, Advanced Grant NonSequeToR # 740516. ...
arXiv:1810.10499v1
fatcat:u4koeg6km5fabms7yfx2qalj54
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing
2018
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
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Knowledge bases (KBs) are paramount in NLP. We employ multiview learning for increasing accuracy and coverage of entity type information in KBs. We rely on two metaviews: language and representation. ...
For representation, we consider representations based on the context distribution of the entity (i.e., on its embedding), on the entity's name (i.e., on its surface form) and on its description in Wikipedia ...
Hazen and the anonymous reviewers for their helpful feedback on earlier drafts. This work was partially supported by the European Research Council, Advanced Grant NonSequeToR # 740516. ...
doi:10.18653/v1/d18-1343
dblp:conf/emnlp/YaghoobzadehS18
fatcat:iz6le43puvb47feoswngprcjby
MetaMIML: Meta Multi-Instance Multi-Label Learning
[article]
2021
arXiv
pre-print
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MetaMIML introduces the context learner with network embedding to capture semantic information of objects of different types, and the task learner to extract the meta knowledge for fast adapting to new ...
To effectively mine interdependent MIML objects of different types, we propose a network embedding and meta learning based approach (MetaMIML). ...
with a task learner for acquiring knowledge from multiple context tasks. ...
arXiv:2111.04112v1
fatcat:rnugpdpl5vdpfn3zhnvyckhwyu
KnowAugNet: Multi-Source Medical Knowledge Augmented Medication Prediction Network with Multi-Level Graph Contrastive Learning
[article]
2022
arXiv
pre-print
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Therefore, to address these limitations, this paper proposes KnowAugNet, a multi-sourced medical knowledge augmented medication prediction network which can fully capture the diverse relations between ...
medical codes and predict medications for patients. ...
While for each specific prediction task, it has different task-specific requirements for the medical codes embedding vectors. ...
arXiv:2204.11736v2
fatcat:vvncqo2tejgwrbpo2cxpstqdt4
Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessment
[article]
2022
arXiv
pre-print
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2022
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
unpublished
doi:10.1109/icassp43922.2022.9746743 fatcat:yaodln4z3bacrnwzh4ttw3ee3q
doi:10.1109/icassp43922.2022.9746743 fatcat:yaodln4z3bacrnwzh4ttw3ee3q
Specifically, we train a Goodness Of Pronunciation feature-based Transformer (GOPT) with multi-task learning. ...
In this work, we explore modeling multi-aspect pronunciation assessment at multiple granularities. ...
To our knowledge, this is the first work studying multi-aspect L2 speaker pronunciation assessment in a multi-granularity fashion. ...
arXiv:2205.03432v1
fatcat:jjj7grjgbjalzh62fg3fpyyare
3M: Multi-loss, Multi-path and Multi-level Neural Networks for speech recognition
[article]
2022
arXiv
pre-print
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In this paper, based on our prior work, we identify and integrate several approaches to achieve further improvements for ASR tasks, which we denote as multi-loss, multi-path and multi-level, summarized ...
Recently, Conformer based CTC/AED model has become a mainstream architecture for ASR. ...
The embedding training loss provides reliable embeddings for the routers. ...
arXiv:2204.03178v2
fatcat:t75odzgsrndjzc22c5sptevmt4
MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning
[article]
2020
arXiv
pre-print
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Multi-hop reasoning approaches over knowledge graphs infer a missing relationship between entities with a multi-hop rule, which corresponds to a chain of relationships. ...
Empirical results show that our multi-chain multi-hop (MCMH) rules result in superior results compared to the standard single-chain approaches, justifying both our formulation of generalized rules and ...
Conclusion We propose a new approach of multi-chain multihop rule learning for knowledge graph completion tasks. ...
arXiv:2010.01735v1
fatcat:s2bbbdo37nc2pekfvip3afea6a
Knowledge Guided Multi-instance Multi-label Learning via Neural Networks in Medicines Prediction
2018
Asian Conference on Machine Learning
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Predicting medicines for patients with co-morbidity has long been recognized as a hard task due to complex dependencies between diseases and medicines. ...
In this paper, we formulate the medicines prediction task in multi-instance multi-label learning framework considering the multi-diagnoses as input instances and multi-medicines as output labels. ...
Acknowledgment This work was supported by Peking University Medicine Seed Fund for Interdisciplinary Research. We also thank NVIDIA for the support of a GPU. ...
dblp:conf/acml/ShangHZWL18
fatcat:b4eaowe2ijavndrsi2ocxfhv5a
GMH: A General Multi-hop Reasoning Model for KG Completion
[article]
2021
arXiv
pre-print
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We argue that there are two key issues for a general multi-hop reasoning model: i) where to go, and ii) when to stop. ...
This results in research efforts on multi-hop reasoning task, which can be formulated as a search process and current models typically perform short distance reasoning. ...
Acknowledgements We sincerely thank Jun Wang and Xu Zhang for their constructive suggestions on this paper. This work was supported by the China Postdoctoral Science Foundation (No.2021TQ0222). ...
arXiv:2010.07620v3
fatcat:d56bqdmtcfeppdfoevlwjmruei
Multi-source knowledge fusion: a survey
2020
World wide web (Bussum)
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On the one hand, the process of multi-source knowledge reasoning can detect conflicts and provide help for knowledge evaluation and verification; on the other hand, the new knowledge acquired by knowledge ...
On this basis, the challenges and future research directions of multisource knowledge fusion in a large-scale knowledge base environment are discussed. ...
Multi-source knowledge fusion is a challenging task. ...
doi:10.1007/s11280-020-00811-0
fatcat:ef5j2sna6fai7k2455yihrrfuq
Adversarial Encoder-Multi-Task-Decoder for Multi-Stage Processes
[article]
2020
arXiv
pre-print
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We address both cases by introducing a framework that combines adversarial autoencoders (AAE), multi-task learning (MTL), and multi-label semi-supervised learning (MLSSL). ...
In multi-stage processes, decisions occur in an ordered sequence of stages. ...
In MTL, multiple related tasks are learned simultaneously so that knowledge can be shared among them. ...
arXiv:2003.06899v1
fatcat:4d4sdhr6xzfd7lzwk4mw3brl5y
Multi-hop Attention Graph Neural Network
[article]
2021
arXiv
pre-print
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On knowledge graph completion MAGNA advances state-of-the-art on WN18RR and FB15k-237 across four different performance metrics. ...
Experimental results on node classification as well as the knowledge graph completion benchmarks show that MAGNA achieves state-of-the-art results: MAGNA achieves up to 5.7 percent relative error reduction ...
We then define the task of knowledge graph completion: Definition 2. ...
arXiv:2009.14332v5
fatcat:vbtttkitzzdezllnx5chtxn3su
MMKG: Multi-Modal Knowledge Graphs
[article]
2019
arXiv
pre-print
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We believe this data set has the potential to facilitate the development of novel multi-modal learning approaches for knowledge graphs.We validate the utility ofMMKG in the sameAs link prediction task ...
We present MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs. ...
Technical Quality of MMKG We provide empirical evidence that knowledge graph completion related tasks can benefit from the multi-modal data of MMKG. ...
arXiv:1903.05485v1
fatcat:mapil3zlcjbipnkzgskufclcaa
μKG: A Library for Multi-source Knowledge Graph Embeddings and Applications
[article]
2022
arXiv
pre-print
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We show that the jointly learned embeddings can greatly help knowledge-powered downstream tasks, such as multi-hop knowledge graph question answering. ...
It is useful for a thorough comparison and analysis of various embedding models and tasks. ...
The joint KG embeddings have demonstrated useful for a variety of downstream tasks such as entity typing and multi-source KG completion [11, 35] . ...
arXiv:2207.11442v2
fatcat:7jbreysht5fjrksczcavb4c7fm
Learning for Multi-Model and Multi-Type Fitting
[article]
2019
arXiv
pre-print
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Comparisons are also made on single-type multi-model fitting tasks with promising results as well. ...
For inference, we apply K-means to cluster the data in the embedded feature space and model selection is enabled by analyzing the K-means residuals. ...
and multi-ellipse segmentation tasks. ...
arXiv:1901.10254v1
fatcat:ap6hpcgohveolajm74mo7m2cyq
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