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Towards information-rich, logical text generation with knowledge-enhanced neural models
[article]
2020
arXiv
pre-print
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However, existing end-to-end neural models suffer from the problem of tending to generate uninformative and generic text because they cannot ground input context with background knowledge. ...
The challenges of knowledge enhanced text generation including how to select the appropriate knowledge from large-scale knowledge bases, how to read and understand extracted knowledge, and how to integrate ...
Neural network models need input data with vector form, while the information stored in structured KB is symbolized. ...
arXiv:2003.00814v1
fatcat:5fllyakwqzf4vnmar3a6zjoewe
Contextual Semantic-Guided Entity-Centric GCN for Relation Extraction
2022
Mathematics
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However, most existing relation extraction models ignore the semantic guidance of contextual information to entity mentions and treat entity mentions in and the textual context of a sentence equally. ...
This model develops a self-attention enhanced neural network to concentrate on the importance and relevance of different words to obtain semantic-guided contextual information. ...
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/math10081344
fatcat:oml3xksrnbcqlawblneosygfv4
Construction and Application of Text Entity Relation Joint Extraction Model Based on Multi-Head Attention Neural Network
2022
Computational Intelligence and Neuroscience
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Entity relationship extraction is one of the key areas of information extraction and is an important research content in the field of natural language processing. ...
Based on the BERT training model architecture, this paper extracts textual entities and relations tasks. ...
joint model of entity recognition and relation extraction for the input sentence. is joint model can overcome the shortcomings of the above serial method, and it follows that the model may have a more ...
doi:10.1155/2022/1530295
pmid:35655501
pmcid:PMC9155959
fatcat:ch24cpgqsvarnbyd24nqsvenjy
Making Document-Level Information Extraction Right for the Right Reasons
[article]
2022
arXiv
pre-print
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Document-level models for information extraction tasks like slot-filling are flexible: they can be applied to settings where information is not necessarily localized in a single sentence. ...
This work studies how to ensure that these models make correct inferences from complex text and make those inferences in an auditable way: beyond just being right, are these models "right for the right ...
Thanks to Scott Rudkin, Gregory Mittl, Raghav Mattay, and Chuan Liang for assistance with the annotation. ...
arXiv:2110.07686v2
fatcat:st5aretytjdspgbfnkwkzfdezu
REKnow: Enhanced Knowledge for Joint Entity and Relation Extraction
[article]
2022
arXiv
pre-print
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Relation extraction is an important but challenging task that aims to extract all hidden relational facts from the text. ...
In this work, we propose a knowledge-enhanced generative model to mitigate these two issues. ...
Knowledge-Enhancement We leverage an entity linking model to ground texts to knowledge bases. In the Relation Classification task, the span of an entity E pos is given. ...
arXiv:2206.05123v2
fatcat:zdm6hq75rbb4ra3lgtwsgzlpte
Noise Robust TTS for Low Resource Speakers using Pre-trained Model and Speech Enhancement
[article]
2020
arXiv
pre-print
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In this paper, the proposed end-to-end speech synthesis model uses both speaker embedding and noise representation as conditional inputs to model speaker and noise information respectively. ...
It is worth investigating how to take advantage of low-quality and low resource voice data which can be easily obtained from the Internet for usage of synthesizing personalized voice. ...
Noise representation condition To model the noise information, we use noise representation (Melspectrogram denoise masks, extracted from the speech enhancement model) as the conditional input of the decoder ...
arXiv:2005.12531v2
fatcat:unc2rvyw6rawve5qqapu2qhu4q
Classifier-adaptation knowledge distillation framework for relation extraction and event detection with imbalanced data
2021
Information Sciences
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Fundamental information extraction tasks, such as relation extraction and event detection, suffer from a data imbalance problem. ...
Like an instructor, the classifier improves the baseline model's ability to extract this sentence-level identification information from raw texts, thus benefiting overall performance. ...
In the input layer, ordinary input embeddings include word embeddings, POS embeddings, and NER embeddings for relation extraction. ...
doi:10.1016/j.ins.2021.05.045
fatcat:6arhjpixqzd5pas7otq7rbjkuy
A Survey of Knowledge-Enhanced Text Generation
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2010.04389v2 fatcat:h2kprdm4knbglljkqarxetwknyOther Versions
To address this issue, researchers have considered incorporating various forms of knowledge beyond the input text into the generation models. ...
Since 2014, various neural encoder-decoder models pioneered by Seq2Seq have been proposed to achieve the goal by learning to map input text to output text. ...
One of the mainstream methods of constructing an internal KG is using open information extraction (OpenIE). ...
arXiv:2010.04389v3
fatcat:vzdtlz4j65g2va7gwkbmzyxkhq
Enhance the Word Vector with Prosodic Information for the Recurrent Neural Network Based TTS System
2016
Interspeech 2016
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Experiment shows that using the enhanced word vectors as an input to the neural network-based acoustic model improves the accuracy of the predicted F0 trajectory. ...
This paper presents a post-filtering approach to enhance the raw word vectors with prosodic information for the TTS task. ...
Acknowledgements We thank the reviewers for the critical comments. ...
doi:10.21437/interspeech.2016-390
dblp:conf/interspeech/WangTY16
fatcat:wa3fmedhvzcyladz6l75pmzdhm
Enhanced Methodology for Relation Descriptor Extraction
2014
International Journal of IT-based Management for Smart Business
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Relation extraction is an interesting filed in information extraction , in which predefined semantic relationships are identified from a text document. ...
This method uses NNLM (natural neural learning model), morphology, grammar and semantic features for relation descriptor extraction. ...
Word embeddings can be useful as input to an NLP model (mostly non-linear) or as additional features to enhance existing systems. ...
doi:10.21742/ijitmsb.2014.1.02
fatcat:mn237wvulvbszi3odf7p5uqb3e
Multi-Stream Semantics-Guided Dynamic Aggregation Graph Convolution Networks to Extract Overlapping Relations
2021
IEEE Access
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The proposed model constructs the entity relation graphs by enumerating the possible candidates and external auxiliary information and adaptively manages the relevant substructure. ...
INDEX TERMS Overlapping relation extraction, multiscale structural information, dynamic aggregation, long distance dependencies, refined graph, relevant substructure. ...
ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their comments and helpful suggestions. ...
doi:10.1109/access.2021.3062231
fatcat:zu4rrzkalfcsxdol7x63lzhadu
An Entity Relation Extraction Method for Few-Shot Learning on the Food Health and Safety Domain
2022
Computational Intelligence and Neuroscience
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This paper proposes an entity relation extraction method FHER for the few-shot learning in the food health and safety domain. ...
The experimental results show that the method can effectively extract domain-related entities and their relations in a small sample size environment. ...
For supervised deep learning models, small sample size can lead to an underfitting of the model. In other words, there is not enough information to train a valid model. ...
doi:10.1155/2022/1879483
pmid:35237307
pmcid:PMC8885244
fatcat:vxswpnbbw5ggdb2e6fbw6jx4ve
RECA: Relation Extraction Based on Cross-Attention Neural Network
2022
Electronics
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Extracting entities and relations, as a crucial part of many tasks in natural language processing, transforms the unstructured text information into structured information and provides corresponding data ...
This work introduces a pre-trained BERT model and a dilated gated convolutional neural network (DGCNN) as an encoder to distinguish the long-range semantics representation from the input sequence. ...
Related Works Extraction of relational triplets from unstructured sentences has always been a fundamental task for information extraction. ...
doi:10.3390/electronics11142161
fatcat:wcarjdx5pvaipk5bic654ee3du
An Analysis of Public Environment-Oriented Marxist Philosophy Content Dissemination
2022
Journal of Environmental and Public Health
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and the advantages of the long-term recurrent convolutional network (LRCN) model in video content recognition, an attention mechanism-based LRCN model is proposed, which simulates the attention characteristics ...
Because of the development of Internet technology, in order to ensure the validity of the uploaded videos related to Marxist philosophy on the platform, combining the research on human visual perception ...
We-media has become an important way for people to exchange information and has a profound impact on information transmission in various fields [12, 13] . ...
doi:10.1155/2022/7873226
pmid:35692662
pmcid:PMC9187483
fatcat:ibbxdopnszdfdcugx6thpsj7xa
An Entity Relation Extraction Method Based on Dynamic Context and Multi-Feature Fusion
2022
Applied Sciences
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Dynamic context selector, a kind of mask idea, will divide the matrix into some regions, selecting the information of region as the input of model dynamically. ...
It is noted that we also use Bi-LSTM_ATT to improve compatibility of longer text in feature extracting layer and enhance context information by combining with the tags of entity in feature fusion layer ...
Informed Consent Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app12031532
fatcat:yea4bfoa4raqbdrj77ch25vchm
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