-
Noever, D.; Ciolino, M.: ¬The Turing deception (2022)
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- Source
- https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
-
Morris, V.: Automated language identification of bibliographic resources (2020)
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- Date
- 2. 3.2020 19:04:22
- Source
- Cataloging and classification quarterly. 58(2020) no.1, S.1-27
-
Luo, L.; Ju, J.; Li, Y.-F.; Haffari, G.; Xiong, B.; Pan, S.: ChatRule: mining logical rules with large language models for knowledge graph reasoning (2023)
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- Date
- 23.11.2023 19:07:22
-
Meng, K.; Ba, Z.; Ma, Y.; Li, G.: ¬A network coupling approach to detecting hierarchical linkages between science and technology (2024)
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- Abstract
- Detecting science-technology hierarchical linkages is beneficial for understanding deep interactions between science and technology (S&T). Previous studies have mainly focused on linear linkages between S&T but ignored their structural linkages. In this paper, we propose a network coupling approach to inspect hierarchical interactions of S&T by integrating their knowledge linkages and structural linkages. S&T knowledge networks are first enhanced with bidirectional encoder representation from transformers (BERT) knowledge alignment, and then their hierarchical structures are identified based on K-core decomposition. Hierarchical coupling preferences and strengths of the S&T networks over time are further calculated based on similarities of coupling nodes' degree distribution and similarities of coupling edges' weight distribution. Extensive experimental results indicate that our approach is feasible and robust in identifying the coupling hierarchy with superior performance compared to other isomorphism and dissimilarity algorithms. Our research extends the mindset of S&T linkage measurement by identifying patterns and paths of the interaction of S&T hierarchical knowledge.
- Source
- Journal of the Association for Information Science and Technology. 75(2023) no.2, S.167-187
-
Corbara, S.; Moreo, A.; Sebastiani, F.: Syllabic quantity patterns as rhythmic features for Latin authorship attribution (2023)
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- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.1, S.128-141
-
Lund, B.D.; Wang, T.; Mannuru, N.R.; Nie, B.; Shimray, S.; Wang, Z.: ChatGPT and a new academic reality : artificial Intelligence-written research papers and the ethics of the large language models in scholarly publishing (2023)
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- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.5, S.570-581
-
Brown, T.B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D.M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; Amodei, D.: Language models are few-shot learners (2020)
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- Abstract
- Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general.
-
Pepper, S.; Arnaud, P.J.L.: Absolutely PHAB : toward a general model of associative relations (2020)
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- Source
- ¬The Mental Lexicon. 15(2020) no.1, S.101-122
-
Soni, S.; Lerman, K.; Eisenstein, J.: Follow the leader : documents on the leading edge of semantic change get more citations (2021)
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- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.4, S.478-492
-
Laparra, E.; Binford-Walsh, A.; Emerson, K.; Miller, M.L.; López-Hoffman, L.; Currim, F.; Bethard, S.: Addressing structural hurdles for metadata extraction from environmental impact statements (2023)
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- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.9, S.1124-1139
-
Al-Khatib, K.; Ghosa, T.; Hou, Y.; Waard, A. de; Freitag, D.: Argument mining for scholarly document processing : taking stock and looking ahead (2021)
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- Pages
- S.56-65.
-
Aizawa, A.; Kohlhase, M.: Mathematical information retrieval (2021)
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- Pages
- S.169-185
-
Pepper, S.: ¬The typology and semantics of binominal lexemes : noun-noun compounds and their functional equivalents (2020)
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- Pages
- IX, 515 S
-
Zhang, Y.; Zhang, C.; Li, J.: Joint modeling of characters, words, and conversation contexts for microblog keyphrase extraction (2020)
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- Source
- Journal of the Association for Information Science and Technology. 71(2020) no.5, S.553-567
-
Escolano, C.; Costa-Jussà, M.R.; Fonollosa, J.A.: From bilingual to multilingual neural-based machine translation by incremental training (2021)
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- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.2, S.190-203
-
Lee, G.E.; Sun, A.: Understanding the stability of medical concept embeddings (2021)
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- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.3, S.346-356
-
Xiang, R.; Chersoni, E.; Lu, Q.; Huang, C.-R.; Li, W.; Long, Y.: Lexical data augmentation for sentiment analysis (2021)
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- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.11, S.1432-1447
-
Andrushchenko, M.; Sandberg, K.; Turunen, R.; Marjanen, J.; Hatavara, M.; Kurunmäki, J.; Nummenmaa, T.; Hyvärinen, M.; Teräs, K.; Peltonen, J.; Nummenmaa, J.: Using parsed and annotated corpora to analyze parliamentarians' talk in Finland (2022)
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- Source
- Journal of the Association for Information Science and Technology. 73(2022) no.2, S.288-302
-
Suissa, O.; Elmalech, A.; Zhitomirsky-Geffet, M.: Text analysis using deep neural networks in digital humanities and information science (2022)
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- Source
- Journal of the Association for Information Science and Technology. 73(2022) no.2, S.268-287
-
Tao, J.; Zhou, L.; Hickey, K.: Making sense of the black-boxes : toward interpretable text classification using deep learning models (2023)
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- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.6, S.685-700
