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  • × author_ss:"Huang, Y."
  1. Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.02 
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    Abstract
    With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end-to-end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro-avgs of , , and on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models.
    Date
    22. 6.2023 14:55:20
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.7, S.759-774
  2. Chen, Z.; Huang, Y.; Tian, J.; Liu, X.; Fu, K.; Huang, T.: Joint model for subsentence-level sentiment analysis with Markov logic (2015) 0.00 
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    Abstract
    Sentiment analysis mainly focuses on the study of one's opinions that express positive or negative sentiments. With the explosive growth of web documents, sentiment analysis is becoming a hot topic in both academic research and system design. Fine-grained sentiment analysis is traditionally solved as a 2-step strategy, which results in cascade errors. Although joint models, such as joint sentiment/topic and maximum entropy (MaxEnt)/latent Dirichlet allocation, are proposed to tackle this problem of sentiment analysis, they focus on the joint learning of both aspects and sentiments. Thus, they are not appropriate to solve the cascade errors for sentiment analysis at the sentence or subsentence level. In this article, we present a novel jointly fine-grained sentiment analysis framework at the subsentence level with Markov logic. First, we divide the task into 2 separate stages (subjectivity classification and polarity classification). Then, the 2 separate stages are processed, respectively, with different feature sets, which are implemented by local formulas in Markov logic. Finally, global formulas in Markov logic are adopted to realize the interactions of the 2 separate stages. The joint inference of subjectivity and polarity helps prevent cascade errors. Experiments on a Chinese sentiment data set manifest that our joint model brings significant improvements.
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.9, S.1913-1922
  3. Song, J.; Huang, Y.; Qi, X.; Li, Y.; Li, F.; Fu, K.; Huang, T.: Discovering hierarchical topic evolution in time-stamped documents (2016) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.4, S.915-927
  4. Huang, Y.; Bu, Y.; Ding, Y.; Lu, W.: From zero to one : a perspective on citing (2019) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 70(2019) no.10, S.1098-1107
  5. Huang, Y.; Cox, A.M.; Sbaffi, L.: Research data management policy and practice in Chinese university libraries (2021) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 72(2021) no.4, S.493-506
  6. Huang, S.; Qian, J.; Huang, Y.; Lu, W.; Bu, Y.; Yang, J.; Cheng, Q.: Disclosing the relationship between citation structure and future impact of a publication (2022) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.7, S.1025-1042
  7. Kulczycki, E.; Huang, Y.; Zuccala, A.A.; Engels, T.C.E.; Ferrara, A.; Guns, R.; Pölönen, J.; Sivertsen, G.; Taskin, Z.; Zhang, L.: Uses of the Journal Impact Factor in national journal rankings in China and Europe (2022) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.12, S.1741-1754
  8. Zhang, L.; Gou, Z.; Fang, Z.; Sivertsen, G.; Huang, Y.: Who tweets scientific publications? : a large-scale study of tweeting audiences in all areas of research (2023) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.13, S.1485-1497

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