Please wait, while we are loading the content...
| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Henriksson, A. Dalianis, H. Kowalski, S. |
| Copyright Year | 2014 |
| Description | Author affiliation: Dept. of Comput. & Syst. Sci., Stockholm Univ., Stockholm, Sweden (Henriksson, A.; Dalianis, H.; Kowalski, S.) |
| Abstract | Creating sufficiently large annotated resources for supervised machine learning, and doing so for every problem and every domain, is prohibitively expensive. Techniques that leverage large amounts of unlabeled data, which are often readily available, may decrease the amount of data that needs to be annotated to obtain a certain level of performance, as well as improve performance when large annotated resources are indeed available. Here, the development of one such method is presented, where semantic features are generated by exploiting the available annotations to learn prototypical (vector) representations of each named entity class in semantic space, constructed by employing a model of distributional semantics (random indexing) over a large, unannotated, in-domain corpus. Binary features that describe whether a given word belongs to a specific named entity class are provided to the learning algorithm; the feature values are determined by calculating the (cosine) distance in semantic space to each of the learned prototype vectors and ascertaining whether they are below or above a given threshold, set to optimize $F_{β}-score.$ The proposed method is evaluated empirically in a series of experiments, where the case is health-record deidentification, a task that involves identifying protected health information (PHI) in text. It is shown that a conditional random fields model with access to the generated semantic features, in addition to a set of orthographic and syntactic features, significantly outperforms, in terms of $F_{1}-score,$ a baseline model without access to the semantic features. Moreover, the quality of the features is further improved by employing a number of slightly different models of distributional semantics in an ensemble. Finally, the way in which the features are generated allows one to optimize them for various $F_{β}-scores,$ giving some degree of control to trade off precision and recall. Methods that are able to improve performance on named entity recognition tasks by exploiting large amounts of unlabeled data may substantially reduce costs involved in creating annotated resources for every domain and every problem. |
| Starting Page | 450 |
| Ending Page | 457 |
| File Size | 439916 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781479956692 |
| DOI | 10.1109/BIBM.2014.6999199 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-11-02 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Context Semantics Prototypes Medical services Syntactics Vectors Indexes |
| Content Type | Text |
| Resource Type | Article |
National Digital Library of India (NDLI) is a virtual repository of learning resources which is not just a repository with search/browse facilities but provides a host of services for the learner community. It is sponsored and mentored by Ministry of Education, Government of India, through its National Mission on Education through Information and Communication Technology (NMEICT). Filtered and federated searching is employed to facilitate focused searching so that learners can find the right resource with least effort and in minimum time. NDLI provides user group-specific services such as Examination Preparatory for School and College students and job aspirants. Services for Researchers and general learners are also provided. NDLI is designed to hold content of any language and provides interface support for 10 most widely used Indian languages. It is built to provide support for all academic levels including researchers and life-long learners, all disciplines, all popular forms of access devices and differently-abled learners. It is designed to enable people to learn and prepare from best practices from all over the world and to facilitate researchers to perform inter-linked exploration from multiple sources. It is developed, operated and maintained from Indian Institute of Technology Kharagpur.
Learn more about this project from here.
NDLI is a conglomeration of freely available or institutionally contributed or donated or publisher managed contents. Almost all these contents are hosted and accessed from respective sources. The responsibility for authenticity, relevance, completeness, accuracy, reliability and suitability of these contents rests with the respective organization and NDLI has no responsibility or liability for these. Every effort is made to keep the NDLI portal up and running smoothly unless there are some unavoidable technical issues.
Ministry of Education, through its National Mission on Education through Information and Communication Technology (NMEICT), has sponsored and funded the National Digital Library of India (NDLI) project.
| Sl. | Authority | Responsibilities | Communication Details |
|---|---|---|---|
| 1 | Ministry of Education (GoI), Department of Higher Education |
Sanctioning Authority | https://www.education.gov.in/ict-initiatives |
| 2 | Indian Institute of Technology Kharagpur | Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project | https://www.iitkgp.ac.in |
| 3 | National Digital Library of India Office, Indian Institute of Technology Kharagpur | The administrative and infrastructural headquarters of the project | Dr. B. Sutradhar bsutra@ndl.gov.in |
| 4 | Project PI / Joint PI | Principal Investigator and Joint Principal Investigators of the project |
Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon |
| 5 | Website/Portal (Helpdesk) | Queries regarding NDLI and its services | support@ndl.gov.in |
| 6 | Contents and Copyright Issues | Queries related to content curation and copyright issues | content@ndl.gov.in |
| 7 | National Digital Library of India Club (NDLI Club) | Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach | clubsupport@ndl.gov.in |
| 8 | Digital Preservation Centre (DPC) | Assistance with digitizing and archiving copyright-free printed books | dpc@ndl.gov.in |
| 9 | IDR Setup or Support | Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops | idr@ndl.gov.in |
|
Loading...
|