Logistic Online Online Learning Methods and Their Application to Incremental Dependency Parsing
(2007) ACL 2007 Student Research Workshop p.49-54- Abstract
- We investigate a family of update methods for online machine learning algorithms for cost-sensitive multiclass and structured classification problems. The update rules are based on multinomial logistic models. The most interesting question for such an approach is how to integrate the cost function into the learning paradigm. We propose a number of solutions to this problem.
To demonstrate the applicability of the algorithms, we evaluated them on a number of classification tasks related to incremental dependency parsing. These tasks were conventional multiclass classification, hiearchical classification, and a structured classification task: complete labeled dependency tree prediction. The performance figures of the... (More) - We investigate a family of update methods for online machine learning algorithms for cost-sensitive multiclass and structured classification problems. The update rules are based on multinomial logistic models. The most interesting question for such an approach is how to integrate the cost function into the learning paradigm. We propose a number of solutions to this problem.
To demonstrate the applicability of the algorithms, we evaluated them on a number of classification tasks related to incremental dependency parsing. These tasks were conventional multiclass classification, hiearchical classification, and a structured classification task: complete labeled dependency tree prediction. The performance figures of the logistic algorithms range from slightly lower to slightly higher than margin-based online algorithms. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/630085
- author
- Johansson, Richard LU
- organization
- publishing date
- 2007
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Machine learning, natural language processing
- host publication
- Proceedings of the ACL 2007 Student Research Workshop
- pages
- 6 pages
- publisher
- Association for Computational Linguistics
- conference name
- ACL 2007 Student Research Workshop
- conference dates
- 2007-06-25 - 2007-06-27
- language
- English
- LU publication?
- yes
- id
- 65378d3c-a74d-4594-956c-92778798bd8c (old id 630085)
- alternative location
- http://www.aclweb.org/anthology-new/P/P07/P07-3009.pdf
- date added to LUP
- 2016-04-04 11:54:12
- date last changed
- 2021-05-06 15:42:03
@inproceedings{65378d3c-a74d-4594-956c-92778798bd8c, abstract = {{We investigate a family of update methods for online machine learning algorithms for cost-sensitive multiclass and structured classification problems. The update rules are based on multinomial logistic models. The most interesting question for such an approach is how to integrate the cost function into the learning paradigm. We propose a number of solutions to this problem.<br/><br> <br/><br> To demonstrate the applicability of the algorithms, we evaluated them on a number of classification tasks related to incremental dependency parsing. These tasks were conventional multiclass classification, hiearchical classification, and a structured classification task: complete labeled dependency tree prediction. The performance figures of the logistic algorithms range from slightly lower to slightly higher than margin-based online algorithms.}}, author = {{Johansson, Richard}}, booktitle = {{Proceedings of the ACL 2007 Student Research Workshop}}, keywords = {{Machine learning; natural language processing}}, language = {{eng}}, pages = {{49--54}}, publisher = {{Association for Computational Linguistics}}, title = {{Logistic Online Online Learning Methods and Their Application to Incremental Dependency Parsing}}, url = {{http://www.aclweb.org/anthology-new/P/P07/P07-3009.pdf}}, year = {{2007}}, }