• Login
    View Item 
    •   Home
    • Research
    • Conference Papers
    • View Item
    •   Home
    • Research
    • Conference Papers
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    An unsupervised learning approach for NER based on online encyclopedia

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    An Unsupervised Learning Approach for NER based on Online Encyclopedia.pdf
    Size:
    687.5Kb
    Format:
    PDF
    Description:
    Accepted manuscript
    Download
    Type
    Conference Paper
    Authors
    Li, Maolong
    Yang, Qiang
    He, Fuzhen
    Li, Zhixu
    Zhao, Pengpeng
    Zhao, Lei
    Chen, Zhigang
    KAUST Department
    King Abdullah University of Science and Technology, Jeddah, Saudi Arabia
    Date
    2019-07-18
    Online Publication Date
    2019-07-18
    Print Publication Date
    2019
    Permanent link to this record
    http://hdl.handle.net/10754/656840
    
    Metadata
    Show full item record
    Abstract
    Named Entity Recognition (NER) is a core task of NLP. State-of-art supervised NER models rely heavily on a large amount of high-quality annotated data, which is quite expensive to obtain. Various existing ways have been proposed to reduce the heavy reliance on large training data, but only with limited effect. In this paper, we propose a novel way to make full use of the weakly-annotated texts in encyclopedia pages for exactly unsupervised NER learning, which is expected to provide an opportunity to train the NER model with no manually-labeled data at all. Briefly, we roughly divide the sentences of encyclopedia pages into two parts simply according to the density of inner url links contained in each sentence. While a relatively small number of sentences with dense links are used directly for training the NER model initially, the left sentences with sparse links are then smartly selected for gradually promoting the model in several self-training iterations. Given the limited number of sentences with dense links for training, a data augmentation method is proposed, which could generate a lot more training data with the help of the structured data of encyclopedia to greatly augment the training effect. Besides, in the iterative self-training step, we propose to utilize a graph model to help estimate the labeled quality of these sentences with sparse links, among which those with the highest labeled quality would be put into our training set for updating the model in the next iteration. Our empirical study shows that the NER model trained with our unsupervised learning approach could perform even better than several state-of-art models fully trained on newswires data.
    Citation
    Li, M., Yang, Q., He, F., Li, Z., Zhao, P., Zhao, L., & Chen, Z. (2019). An Unsupervised Learning Approach for NER Based on Online Encyclopedia. Lecture Notes in Computer Science, 329–344. doi:10.1007/978-3-030-26072-9_25
    Sponsors
    This research is partially supported by National Natural Science Foundation of China (Grant No. 61632016, 61572336, 61572335, 61772356), and the Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA520003, 18KJA520010).
    Publisher
    Springer International Publishing
    Conference/Event name
    3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019
    DOI
    10.1007/978-3-030-26072-9_25
    Additional Links
    http://link.springer.com/10.1007/978-3-030-26072-9_25
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-26072-9_25
    Scopus Count
    Collections
    Conference Papers

    entitlement

     
    DSpace software copyright © 2002-2021  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.