NDLI: Semi-Supervised Sequence Labeling with Self-Learned Features

Content Provider	IEEE Xplore Digital Library
Author	Yanjun Qi Kuksa, P. Collobert, R. Sadamasa, K. Kavukcuoglu, K. Weston, J.
Copyright Year	2009
Abstract	Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of labeled words. To tackle this issue, we propose a semi-supervised approach to improve the sequence labeling procedure in IE through a class of algorithms with {\em self-learned features} (SLF). A supervised classifier can be trained with annotated text sequences and used to classify each word in a large set of unannotated sentences. By averaging predicted labels over all cases in the unlabeled corpus, SLF training builds class label distribution patterns for each word (or word attribute) in the dictionary and re-trains the current model iteratively adding these distributions as extra word {\em features}. Basic SLF models how likely a word could be assigned to target class types. Several extensions are proposed, such as learning words' class boundary distributions. SLF exhibits robust and scalable behaviour and is easy to tune. We applied this approach on four classical IE tasks: named entity recognition (German and English), part-of-speech tagging (English) and one gene name recognition corpus. Experimental results show effective improvements over the supervised baselines on all tasks. In addition, when compared with the closely related self-training idea, this approach shows favorable advantages.
Starting Page	428
Ending Page	437
File Size	299815
Page Count	10
File Format	PDF
ISBN	9781424452422
ISSN	15504786
DOI	10.1109/ICDM.2009.40
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Labeling USA Councils Data mining Natural language processing Computer science Predictive models Tagging Neural networks Machine learning National electric code information extraction semi-supervised learning semi-supervised feature learning structural output learning sequence labeling self-learned features
Content Type	Text
Resource Type	Article

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

Semi-supervised sequence labeling with self-learned features.

2009 ninth ieee international conference on data mining semi-supervised sequence labeling with self-learned features.

Semi-supervised Multitask Learning for Sequence Labeling

Semi-supervised learning for sequence labeling using conditional random fields

Recursive Feature Extraction for Ordinal Regression

Discover trending domains using fusion of supervised machine learning with natural language processing

Feature-based Thai unknown word boundary identification using Winnow

Semi-Supervised Method for Multi-category Emotion Recognition in Tweets

Labeling Turkish news stories with CRF

Semi-Supervised Sequence Labeling with Self-Learned Features

Similar Documents

Semi-supervised sequence labeling with self-learned features.

2009 ninth ieee international conference on data mining semi-supervised sequence labeling with self-learned features.

Semi-supervised Multitask Learning for Sequence Labeling

Semi-supervised learning for sequence labeling using conditional random fields

Recursive Feature Extraction for Ordinal Regression

Discover trending domains using fusion of supervised machine learning with natural language processing

Feature-based Thai unknown word boundary identification using Winnow

Semi-Supervised Method for Multi-category Emotion Recognition in Tweets

Labeling Turkish news stories with CRF

Semi-Supervised Sequence Labeling with Self-Learned Features