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Jiang, Z.; Gu, Q.; Yin, Y.; Wang, J.; Chen, D.: GRAW+ : a two-view graph propagation method with word coupling for readability assessment (2019)
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- Abstract
- Existing methods for readability assessment usually construct inductive classification models to assess the readability of singular text documents based on extracted features, which have been demonstrated to be effective. However, they rarely make use of the interrelationship among documents on readability, which can help increase the accuracy of readability assessment. In this article, we adopt a graph-based classification method to model and utilize the relationship among documents using the coupled bag-of-words model. We propose a word coupling method to build the coupled bag-of-words model by estimating the correlation between words on reading difficulty. In addition, we propose a two-view graph propagation method to make use of both the coupled bag-of-words model and the linguistic features. Our method employs a graph merging operation to combine graphs built according to different views, and improves the label propagation by incorporating the ordinal relation among reading levels. Experiments were conducted on both English and Chinese data sets, and the results demonstrate both effectiveness and potential of the method.
- Date
- 15. 4.2019 13:46:22
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Mao, J.; Xu, W.; Yang, Y.; Wang, J.; Yuille, A.L.: Explain images with multimodal recurrent neural networks (2014)
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- Abstract
- In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel sentence descriptions to explain the content of images. It directly models the probability distribution of generating a word given previous words and the image. Image descriptions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on three benchmark datasets (IAPR TC-12 [8], Flickr 8K [28], and Flickr 30K [13]). Our model outperforms the state-of-the-art generative method. In addition, the m-RNN model can be applied to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval.
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Shen, R.; Wang, J.; Fox, E.A.: ¬A Lightweight Protocol between Digital Libraries and Visualization Systems (2002)
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- Date
- 22. 2.2003 17:25:39
22. 2.2003 18:15:14
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Wang, J.; Clements, M.; Yang, J.; Vries, A.P. de; Reinders, M.J.T.: Personalization of tagging systems (2010)
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- Abstract
- Social media systems have encouraged end user participation in the Internet, for the purpose of storing and distributing Internet content, sharing opinions and maintaining relationships. Collaborative tagging allows users to annotate the resulting user-generated content, and enables effective retrieval of otherwise uncategorised data. However, compared to professional web content production, collaborative tagging systems face the challenge that end-users assign tags in an uncontrolled manner, resulting in unsystematic and inconsistent metadata. This paper introduces a framework for the personalization of social media systems. We pinpoint three tasks that would benefit from personalization: collaborative tagging, collaborative browsing and collaborative search. We propose a ranking model for each task that integrates the individual user's tagging history in the recommendation of tags and content, to align its suggestions to the individual user preferences. We demonstrate on two real data sets that for all three tasks, the personalized ranking should take into account both the user's own preference and the opinion of others.
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Lu, C.; Bu, Y.; Wang, J.; Ding, Y.; Torvik, V.; Schnaars, M.; Zhang, C.: Examining scientific writing styles from the perspective of linguistic complexity : a cross-level moderation model (2019)
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Wang, J.: ¬An extensive study on automated Dewey Decimal Classification (2009)
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- Abstract
- In this paper, we present a theoretical analysis and extensive experiments on the automated assignment of Dewey Decimal Classification (DDC) classes to bibliographic data with a supervised machine-learning approach. Library classification systems, such as the DDC, impose great obstacles on state-of-art text categorization (TC) technologies, including deep hierarchy, data sparseness, and skewed distribution. We first analyze statistically the document and category distributions over the DDC, and discuss the obstacles imposed by bibliographic corpora and library classification schemes on TC technology. To overcome these obstacles, we propose an innovative algorithm to reshape the DDC structure into a balanced virtual tree by balancing the category distribution and flattening the hierarchy. To improve the classification effectiveness to a level acceptable to real-world applications, we propose an interactive classification model that is able to predict a class of any depth within a limited number of user interactions. The experiments are conducted on a large bibliographic collection created by the Library of Congress within the science and technology domains over 10 years. With no more than three interactions, a classification accuracy of nearly 90% is achieved, thus providing a practical solution to the automatic bibliographic classification problem.
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Hicks, D.; Wang, J.: Coverage and overlap of the new social sciences and humanities journal lists (2011)
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- Date
- 22. 1.2011 13:21:28
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He, R.; Wang, J.; Tian, J.; Chu, C.-T.; Mauney, B.; Perisic, I.: Session analysis of people search within a professional social network (2013)
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- Date
- 19. 4.2013 20:31:22
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Wang, J.; Halffman, W.; Zhang, Y.H.: Sorting out journals : the proliferation of journal lists in China (2023)
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- Date
- 22. 9.2023 16:39:23
