Home Catalogue search

eng

Refine your search:

Search in the Catalogues and Directories






	Sort by
Simple Search

Page: 1 2 3 4 5

Hits 1 – 20 of 92

1	Integrating a Phrase Structure Corpus Grammar and a Lexical-Semantic Network: the HOLINET Knowledge Graph
	Prost, Jean-Philippe
	In: Proceedings of LREC 2022 ; https://hal-amu.archives-ouvertes.fr/hal-03655636 ; Proceedings of LREC 2022, Jun 2022, Marseille, France (2022)
	BASE
	Show details

2	Structure and Learning (Dagstuhl Seminar 21362)
	Dong, Tiansi; Rettinger, Achim; Tang, Jie. - : Dagstuhl Reports. DagRep, Volume 11, Issue 8, 2022
	BASE
	Show details

3	Mastering Occupation Data ...
	Winzenried, Stefan. - : Zenodo, 2022
	BASE
	Show details

4	LinkingPark: Automatic Semantic Table Interpretation Software ...
	Chen, Shuang; Karaoglu, Alperen; Negreanu, Carina. - : Zenodo, 2022
	BASE
	Show details

5	LinkingPark: Automatic Semantic Table Interpretation Software ...
	Chen, Shuang; Karaoglu, Alperen; Negreanu, Carina. - : Zenodo, 2022
	BASE
	Show details

6	Mastering Occupation Data ...
	Winzenried, Stefan. - : Zenodo, 2022
	BASE
	Show details

7	Structure and Learning (Dagstuhl Seminar 21362) ...
	Dong, Tiansi; Rettinger, Achim; Tang, Jie. - : Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022
	BASE
	Show details

8	MEduKG: A Deep-Learning-Based Approach for Multi-Modal Educational Knowledge Graph Construction
	Nan Li; Qiang Shen; Rui Song; Yang Chi; Hao Xu
	In: Information; Volume 13; Issue 2; Pages: 91 (2022)
	BASE
	Show details

9	Knowledge Representation and Reasoning with an Extended Dynamic Uncertain Causality Graph under the Pythagorean Uncertain Linguistic Environment
	Yu-Jie Zhu; Wei Guo; Hu-Chen Liu
	In: Applied Sciences; Volume 12; Issue 9; Pages: 4670 (2022)
	BASE
	Show details

10	A Multi-Entity Knowledge Joint Extraction Method of Communication Equipment Faults for Industrial IoT
	Kun Liang; Baoxian Zhou; Yiying Zhang; Yeshen He; Xiaoyan Guo; Bo Zhang
	In: Electronics; Volume 11; Issue 7; Pages: 979 (2022)
	BASE
	Show details

11	CSKG: The Commonsense Knowledge Graph ...
	Ilievski, Filip. - : Zenodo, 2022
	BASE
	Show details

12	CSKG: The Commonsense Knowledge Graph ...
	Ilievski, Filip. - : Zenodo, 2022
	BASE
	Show details

13	Neural-based Knowledge Transfer in Natural Language Processing
	Wang, Chao. - 2022
	Abstract: In Natural Language Processing (NLP), neural-based knowledge transfer, which is to transfer out-of-domain (OOD) knowledge to task-specific neural networks, has been applied to many NLP tasks. To further explore neural-based knowledge transfer in NLP, in this dissertation, we consider both structured OOD knowledge and unstructured OOD knowledge, and deal with several representative NLP tasks. For structured OOD knowledge, we study the neural-based knowledge transfer in Machine Reading Comprehension (MRC). In single-passage MRC tasks, to bridge the gap between MRC models and human beings, which is mainly reflected in the hunger for data and the robustness to noise, we integrate the neural networks of MRC models with the general knowledge of human beings embodied in knowledge bases. On the one hand, we propose a data enrichment method, which uses WordNet to extract inter-word semantic connections as general knowledge from each given passage-question pair. On the other hand, we propose a novel MRC model named Knowledge Aided Reader (KAR), which explicitly uses the above extracted general knowledge to assist its attention mechanisms. According to the experimental results, KAR is comparable in performance with the state-of-the-art MRC models, and significantly more robust to noise than them. On top of that, when only a subset (20%-80%) of the training examples are available, KAR outperforms the state-of-the-art MRC models by a large margin, and is still reasonably robust to noise. In multi-hop MRC tasks, to probe the strength of Graph Neural Networks (GNNs), we propose a novel multi-hop MRC model named Graph Aided Reader (GAR), which uses GNN methods to perform multi-hop reasoning, but is free of any pre-trained language model and completely end-to-end. For graph construction, GAR utilizes the topic-referencing relations between passages and the entity-sharing relations between sentences, which is aimed at obtaining the most sensible reasoning clues. For message passing, GAR simulates a top-down reasoning and a bottom-up reasoning, which is aimed at making the best use of the above obtained reasoning clues. According to the experimental results, GAR even outperforms several competitors relying on pre-trained language models and filter-reader pipelines, which implies that GAR benefits a lot from its GNN methods. On this basis, GAR can further benefit from applying pre-trained language models, but pre-trained language models can mainly facilitate the within-passage reasoning rather than cross-passage reasoning of GAR. Moreover, compared with the competitors constructed as filter-reader pipelines, GAR is not only easier to train, but also more applicable to the low-resource cases. For unstructured OOD knowledge, we study the neural-based knowledge transfer in Natural Language Understanding (NLU), and focus on the neural-based knowledge transfer between languages, which is also known as Cross-Lingual Transfer Learning (CLTL). To facilitate the CLTL of NLU models, especially the CLTL between distant languages, we propose a novel CLTL model named Translation Aided Language Learner (TALL), where CLTL is integrated with Machine Translation (MT). Specifically, we adopt a pre-trained multilingual language model as our baseline model, and construct TALL by appending a decoder to it. On this basis, we directly fine-tune the baseline model as an NLU model to conduct CLTL, but put TALL through an MT-oriented pre-training before its NLU-oriented fine-tuning. To make use of unannotated data, we implement the recently proposed Unsupervised Machine Translation (UMT) technique in the MT-oriented pre-training of TALL. According to the experimental results, the application of UMT enables TALL to consistently achieve better CLTL performance than the baseline model without using more annotated data, and the performance gain is relatively prominent in the case of distant languages.
	Keyword: Cross-lingual transfer learning; Graph neural network; Information technology; Knowledge base; Knowledge graph; Knowledge transfer; Machine Reading Comprehension; Multi-hop reasoning; Natural Language Processing; Natural language understanding; Neural network; unsupervised machine translation
	URL: http://hdl.handle.net/10315/39096
	BASE
	Hide details

14	Linking an Abstract Corpus Grammar to a Lexical Semantic Network
	Prost, Jean-Philippe
	In: https://hal.archives-ouvertes.fr/hal-03552630 ; [Research Report] Laboratoire Parole et Langage – Université d’Aix-Marseille. 2021 (2021)
	BASE
	Show details

15	Coupler syntaxe et sémantique dans une même base de connaissances linguistiques
	Prost, Jean-Philippe
	In: https://hal.archives-ouvertes.fr/hal-03552622 ; [Rapport de recherche] Laboratoire Parole et Langage – Université d’Aix-Marseille. 2021 (2021)
	BASE
	Show details

16	Geographic Question Answering with Spatially-Explicit Machine Learning Models
	Mai, Gengchen. - : eScholarship, University of California, 2021
	BASE
	Show details

17	Knowledge Graph publication and browsing using Neo4J
	Atemezing, Ghislain Auguste; Huynh, Anh
	In: The 1st workshop on Squaring the circle on graphs ; https://hal.archives-ouvertes.fr/hal-03453479 ; The 1st workshop on Squaring the circle on graphs, Sep 2021, Amsterdam, Netherlands (2021)
	BASE
	Show details

18	Utilising knowledge graph embeddings for data-to-text generation
	Arcan, Mihael; Pasricha, Nivranshu; Buitelaar, Paul. - : Association for Computational Linguistics, 2021
	BASE
	Show details

19	Exploring Construction of a Company Domain-Specific Knowledge Graph from Financial Texts Using Hybrid Information Extraction
	Jen, Chun-Heng. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021
	BASE
	Show details

20	Leveraging literals for knowledge graph embeddings ...
	Gesese, Genet Asefa. - : CEUR-WS.org, 2021
	BASE
	Show details

Page: 1 2 3 4 5

© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern