1 |
P. Shi and J. Lin, Simple BERT models for relation extraction and semantic role labeling, arXiv preprint, CoRR, 2019, arXiv: 1904.05255.
|
2 |
M. Sun et al., Logician: A unified end-to-end neural approach for open-domain information extraction, in Proc. Web Search Data Min. (Los Angeles, CA, USA), Feb. 2018, pp. 556-564.
|
3 |
S. Auer et al., DBpedia: A nucleus for a web of open data, in Proc. Int. Semantic Web Conf. (Busan, Republic of Korea), Nov. 2007, pp. 722-735.
|
4 |
F. M. Suchanek, G. Kasneci, and G. Weikum, YAGO: A core of semantic knowledge, in Proc. Int. Conf. WWW (Banff, Canada), May 2007, pp. 697-706.
|
5 |
B. D. Trisedya, J. Qi, R. Zhang, and W. Wang, GTR-LSTM: A triple encoder for sentence generation from RDF data, in Proc. Annu. Meet. Assoc. Comput. Linguistics (Melbourne, Australia), July 2018, pp. 1627-1637.
|
6 |
R. Jozefowicz, W. Zaremba, and I. Sutskever, An empirical exploration of recurrent network architectures, in Proc. Int. Conf. Mach. Learn. (Lille, France), June 2015, pp. 2342-2350.
|
7 |
M. F. Y. Ghadikolaie, E. Kabir, and F. Razzazi, Sub-word based offline handwritten farsi word recognition using recurrent neural network, ETRI J. 38 (2016), no. 4, 703-713.
DOI
|
8 |
Y. Bengio, P. Simard, and P. Frasconi, Learning long-term dependencies with gradient descent is difficult, IEEE Trans. Neural Netw. 5 (1994), no. 2, 157-166.
DOI
|
9 |
S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural Comput. 9 (1997), no. 8, 1735-1780.
DOI
|
10 |
F. A. Gers and J. Schmidhuber, Recurrent nets that time and count, in Proc. Int. Joint Conf. Neural Netw. (Como, Italy), July 2000, pp. 189-194.
|
11 |
A. Graves and J. Schmidhuber, Framewise phoneme classification with bidirectional LSTM and other neural network architectures, Neural Netw. 18 (2005), no. 5-6, 602-610.
DOI
|
12 |
A. Graves, N. Jaitly, and A. Mohamed, Hybrid speech recognition with deep bidirectional LSTM, in Proc. IEEE Workshop Autom. Speech Recognit. Underst. (Olomouc, Czech Republic), Dec. 2013, pp. 273-278.
|
13 |
A. Parikh et al., A decomposable attention model for natural language inference, in Proc. Empir. Methods Nat. Lang. Process. (Austin, TX, USA), Nov. 2016, pp. 2249-2255.
|
14 |
D. Vrandecic and M. Krotzsch, Wikidata: A free collaborative knowledgebase, Commun. ACM 57 (2014), no. 10, 78-85.
DOI
|
15 |
N. Kolitsas, O.-E. Ganea, and T. Hofmann, End-to-end neural entity linking, in Proc. Conf. Comput. Nat. Lang. Learn. (Brussels, Belgium), Aug. 2018, pp. 519-529.
|
16 |
F. U. M. Ullah et al., Short-term prediction of residential power energy consumption via CNN and multilayer bi-directional LSTM Networks, IEEE Access 8 (2019), 123369-123380.
DOI
|
17 |
A. M. Rush, S. Chopra, and J. Weston, A neural attention model for abstractive sentence summarization, in Proc. Empir. Methods Nat. Lang. Process. (Lisbon, Portugal), Sept. 2015, pp. 379-389.
|
18 |
M. P. Akhter et al., Document-level text classification using single-layer multisize filters convolutional neural network, IEEE Access 8 (2020), 42689-42707.
DOI
|
19 |
B. D. Trisedya, J. Qi, and R. Zhang, Entity alignment between knowledge graphs using attribute embeddings, in Proc. AAAI Conf. on Artif. Intell. (Honolulu, HI, USA), July 2019, pp. 297-304.
|
20 |
T. Mikolov et al., Distributed representations of words and phrases and their compositionality, in Proc. Neural Inf. Process. Syst. (Lake Tahoe, NV, USA), Dec. 2013, pp. 3111-3119.
|
21 |
S. Riedel, L. Yao, and A. McCallum, Modeling relations and their mentions without labeled text, in Proc. Joint Eur. Conf. Mach. Learn. Knowl. Discov. Databases (Barcelona, Spain), Sept. 2010, pp. 148-163.
|
22 |
O. Lehmberg et al., A large public corpus of web tables containing time and context metadata, in Proc. Int. Conf. Companion WWW (Montreal, Canada), Apr. 2016, pp. 75-76.
|
23 |
M. Mintz et al., Distant supervision for relation extraction without labeled data, in Proc. Joint Conf. Assoc. Comput. Linguistics & Int. Joint Conf. Natural Lang. Process. AFNLP (Suntec, Singapore), Aug. 2009, pp. 1003-1011.
|
24 |
D. Zeng et al., Distant supervision for relation extraction via piecewise convolutional neural networks, in Proc. Conf. Empir. Methods Nat. Lang. Process. (Lisbon, Portugal), Sept. 2015, pp. 1753-1762.
|
25 |
M. J. Cafarella et al., WebTables: Exploring the power of tables on the web, in Proc. Very Large Data Base Endowment (Auckland, New Zealand), Aug. 2008, pp. 538-549.
|
26 |
M. Banko et al., Open information extraction from the web, in Proc. Int. Joint Conf. Artif. Intell. (Hyderabad, India), Jan. 2007, pp. 2670-2676.
|
27 |
W. Khan et al., Deep recurrent neural networks with word embeddings for Urdu named entity recognition, ETRI J. 42 (2020), no. 1, 90-100.
DOI
|
28 |
M. Schuster and K. K. Paliwal, Bidirectional recurrent neural networks, IEEE Trans. Signal Process. 45 (1997), no. 11, 2673-2681.
DOI
|
29 |
Z. Jiang, P. Yin, and G. Neubig, Improving open information extraction via iterative rank-aware learning, in Proc. Annu. Meet. Assoc. Comput. Linguistics (Florence, Italy), May 2019, pp. 5295-5300.
|
30 |
Y. Lin et al., Neural relation extraction with selective attention over instances, in Proc. Annu. Meet. Assoc. Comput. Linguistics (Berlin, Germany), Aug. 2016, pp. 2124-2133.
|
31 |
A. Fader, S. Soderland, and O. Etzioni, Identifying relations for open information extraction, in Proc. Conf. Empir. Methods Nat. Lang. Process. (Edinburgh, UK), July 2011, pp. 1535-1545.
|
32 |
L. D. Corro and R. Gemulla, ClausIE: Clause-based open information extraction, in Proc. Int. Conf. WWW (Rio de Janeiro, Brazil), May 2013, pp. 355-366.
|
33 |
K. Gashteovski, R. Gemulla, and L. D. Corro, MinIE: Minimizing facts in open information extraction, in Proc. Conf. Empir. Methods Nat. Lang. Process. (Copenhagen, Denmark), Sept. 2017, pp. 2630-2640.
|
34 |
G. Stanovsky et al., Supervised open information extraction, in Proc. N. Am. Chapter Assoc. Comput. Linguistics: Hum. Lang. Technol. (New Orleans, LA, USA), June 2018, pp. 885-895.
|
35 |
L. Cui, F. Wei, and M. Zhou, Neural open information extraction, in Proc. Annu. Meet. Assoc. Comput. Linguistics (Melbourne, Australia), May 2018, pp. 407-413.
|
36 |
S. Jia, Y. Xiang, and X. Chen, Supervised neural models revitalize the open relation extraction, arXiv preprint, CoRR, 2018, arXiv: 1809.09408.
|
37 |
G. Liu and J. Guo, Bidirectional LSTM with attention mechanism and convolutional layer for text classification, Neurocomputing 337 (2019), 325-338.
DOI
|
38 |
V. Mnih et al., Recurrent models of visual attention, in Proc. Int. Conf. Neural Inf. Process. Syst. (Montreal, Canada), Dec. 2014, pp. 2204-2212.
|
39 |
D. Bahdanau, K. Cho, and Y. Bengio, Neural machine translation by jointly learning to align and translate, arXiv preprint, CoRR, 2014, arXiv: 1409.0473.
|
40 |
Z. Zhang, Y. Zou, and C. Gan, Textual sentiment analysis via three different attention convolutional neural networks and cross-modality consistent regression, Neurocomputing 275 (2018), 1407-1415.
DOI
|
41 |
A. Vaswani et al., Attention is all you need, in Proc. Conf. Neural Inf. Process. Syst. (Long Beach, CA, USA), Dec. 2017, pp. 6000-6010.
|
42 |
G. Angeli, M. J. J. Premkumar, and C. D. Manning, Leveraging linguistic structure for open domain information extraction, in Proc. Assoc. Comput. (Beijing, China), July 2015, pp. 344-354.
|
43 |
Y. Papanikolaou, I. Roberts, and A. Pierleoni, Deep bidirectional transformers for relation extraction without supervision, arXiv preprint, CoRR, 2019, arXiv: 1911.00313.
|
44 |
J. Nivre et al., Universal dependencies v1: A multilingual treebank collection, in Proc. Int. Conf. Lang. Resour. Eval. (Portoroz, Slovenia), May 2016, pp. 1659-1666.
|
45 |
D. Chen and C. D. Manning, A fast and accurate dependency parser using neural networks, in Proc. Conf. Empir. Methods Nat. Lang. Process. (Doha, Qatar), Oct. 2014, pp. 740-750.
|
46 |
J. Cheng, L. Dong, and M. Lapata, Long short-term memorynetworks for machine reading, in Proc. Conf. Empir. Methods Nat. Lang. Process. (Austin, TX, USA), Nov. 2016, pp. 551-561.
|
47 |
B. Fetahu, A. Anand, and M. Koutraki, TableNet: An approach for determining fine-grained relations for wikipedia tables, in Proc. Int WWW Conf. (San Francisco, CA, USA), May 2019, pp. 2736-2742.
|
48 |
S. Riedel et al., Relation extraction with matrix factorization and universal schemas, in Proc. N. Am. Chapter Assoc. Comput. Linguistics: Hum. Lang. Technol. (Atlanta, GA, USA), June 2013, pp. 74-84.
|
49 |
P. Zhou et al., Distant supervision for relation extraction with hierarchical selective attention, Neural Netw. 108 (2018), 240-247.
DOI
|
50 |
M. Schmitz et al., Open language learning for information extraction, in Proc. Conf. Empir. Methods Nat. Lang. Process. (Jeju, Republic of Korea), July 2012, pp. 523-534.
|
51 |
B. D. Trisedya et al., Neural relation extraction for knowledge base enrichment, in Proc. Annu. Meet. Assoc. Comput. Linguistics (Florence, Italy), July 2019, pp. 229-240.
|
52 |
J. Devlin et al., BERT: Pre-training of deep bidirectional transformers for language understanding, in Proc. N. Am. Chapter Assoc. Comput. Linguistics: Hum. Lang. Technol. (Minneapolis, MN, USA), May 2019, pp. 4171-4186.
|
53 |
T. Mikolov et al., Efficient estimation of word representations in vector space, arXiv preprint, CoRR, 2013, arXiv: 1301.3781.
|
54 |
F. A. Gers, J. Schmidhuber, and F. Cummins, Learning to forget: Continual prediction with LSTM, in Proc. Int. Conf. Artif. Neural Netw. (Edinburgh, UK), Oct. 1999, pp. 850-855.
|
55 |
M. D. Marneffe et al., Universal Stanford dependencies: A cross-linguistic typology, in Proc. Int. Conf. Lang. Resour. Eval. (Reykjavik, Iceland), May 2014, pp. 4585-4592.
|
56 |
N. Nakashole, G. Weikum, and F. Suchanek, PATTY: A taxonomy of relational patterns with semantic types, in Proc. Conf. Empir. Methods Nat. Lang. Process. (Jeju, Republic of Korea), July 2012, pp. 1135-1145.
|
57 |
D. Klein and C. D. Manning, Accurate unlexicalized parsing, in Proc. Annu. Meet. Assoc. Comput. Linguistics (Sapporo, Japan), 34 (2003), pp. 423-430.
|
58 |
J. Pennington, R. Socher, and C. D. Manning, GloVe: Global vectors for word representation, in Proc. Conf. Empir. Methods Nat. Lang. Process. (Doha, Qatar), Oct. 2014, pp. 1532-1543.
|