Search (6 results, page 1 of 1)

  • × author_ss:"Wu, J."
  • × language_ss:"e"
  1. Zhao, G.; Wu, J.; Wang, D.; Li, T.: Entity disambiguation to Wikipedia using collective ranking (2016) 0.02 
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    Abstract
    Entity disambiguation is a fundamental task of semantic Web annotation. Entity Linking (EL) is an essential procedure in entity disambiguation, which aims to link a mention appearing in a plain text to a structured or semi-structured knowledge base, such as Wikipedia. Existing research on EL usually annotates the mentions in a text one by one and treats entities independent to each other. However this might not be true in many application scenarios. For example, if two mentions appear in one text, they are likely to have certain intrinsic relationships. In this paper, we first propose a novel query expansion method for candidate generation utilizing the information of co-occurrences of mentions. We further propose a re-ranking model which can be iteratively adjusted based on the prediction in the previous round. Experiments on real-world data demonstrate the effectiveness of our proposed methods for entity disambiguation.
    Date
    24.10.2016 19:22:54
    Type
    a
  2. Radford, A.; Wu, J.; Child, R.; Luan, D.; Amode, D.; Sutskever, I.: Language models are unsupervised multitask learners 0.00 
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    Abstract
    Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on task-specific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset - matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations.
    Type
    a
  3. 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) 0.00 
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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.
    Type
    a
  4. He, C.; Wu, J.; Zhang, Q.: Proximity-aware research leadership recommendation in research collaboration via deep neural networks (2022) 0.00 
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    Abstract
    Collaborator recommendation is of great significance for facilitating research collaboration. Proximities have been demonstrated to be significant factors and determinants of research collaboration. Research leadership is associated with not only the capability to integrate resources to launch and sustain the research project but also the production and academic impact of the collaboration team. However, existing studies mainly focus on social or cognitive proximity, failing to integrate critical proximities comprehensively. Besides, existing studies focus on recommending relationships among all the coauthors, ignoring leadership in research collaboration. In this article, we propose a proximity-aware research leadership recommendation (PRLR) model to systematically integrate critical node attribute information (critical proximities) and network features to conduct research leadership recommendation by predicting the directed links in the research leadership network. PRLR integrates cognitive, geographical, and institutional proximity as node attribute information and constructs a leadership-aware coauthorship network to preserve the research leadership information. PRLR learns the node attribute information, the local network features, and the global network features with an autoencoder model, a joint probability constraint, and an attribute-aware skip-gram model, respectively. Extensive experiments and ablation studies have been conducted, demonstrating that PRLR significantly outperforms the state-of-the-art collaborator recommendation models in research leadership recommendation.
    Type
    a
  5. Wu, J.; Yun, G.: From modernization to neoliberalism? : how IT opinion leaders imagine the information society (2018) 0.00 
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    Abstract
    Before the information society becomes reality, it exits as discourses and arguments. These narratives shape people's expectations, imaginations, and understandings of the concrete form of information society. The article first reviews some recent literature on the social and cultural history of the Internet and information technologies. Then, we will critically examine some prominent discourses on new information technology, especially the Internet, by cultural intermediaries in China. We hope to understand how the different imaginations of information society come into being, their internal diversity, their sources of influence, and how they help imagine a social form in which these technologies shape, belong, and work well
    Type
    a
  6. He, C.; Wu, J.; Zhang, Q.: Research leadership flow determinants and the role of proximity in research collaborations (2020) 0.00 
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    Abstract
    Characterizing the leadership in research is important to revealing the interaction pattern and organizational structure through research collaboration. This research defines the leadership role based on the corresponding author's affiliation, and presents the first quantitative research on the factors and evolution of 5 proximity dimensions (geographical, cognitive, institutional, social, and economic) of research leadership. The data to capture research leadership consist of a set of multi-institution articles in the fields of "Life Sciences & Biomedicine," "Technology," "Physical Sciences," "Social Sciences," and "Humanities & Arts" during 2013-2017 from the Web of Science Core Citation Database. A Tobit regression-based gravity model indicates that the mass of research leadership of both the leading and participating institutions and the geographical, cognitive, institutional, social, and economic proximities are important factors for the flow of research leadership among Chinese institutions. In general, the effect of these proximities for research leadership flow has been declining recently. The outcome of this research sheds light on the leadership evolution and flow among Chinese institutions, and thus can provide evidence and support for grant allocation policies to facilitate scientific research and collaborations.
    Type
    a

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