抄録
In the relation extraction of semantic relations, it is not uncommon to face settings in which the training data provides very few instances of some relation classes. This is mostly due to the high cost of producing such data and to the class imbalance problem, which may result in some classes presenting small frequencies even with a large annotated corpus. This work thus presents a semi-supervised bootstrapped method to expand this initial training dataset, using pattern matching to extract new candidate instances from the Web. The core of this process uses a multiview feature distance-based framework, which allows quantitative and qualitative analysis of intermediate steps of the process. Experimental results show that this framework provides better results in the relation classification task than the baseline, and the bootstrapped architecture improves the relation classification task as a whole for these low-frequency semantic relations settings.
| 元の言語 | English |
|---|---|
| ホスト出版物のタイトル | Proceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011 |
| ページ | 59-66 |
| ページ数 | 8 |
| DOI | |
| 出版物ステータス | Published - 2011 |
| 外部発表 | Yes |
| イベント | 5th Annual IEEE International Conference on Semantic Computing, ICSC 2011 - Palo Alto, CA 継続期間: 2011 9 18 → 2011 9 21 |
Other
| Other | 5th Annual IEEE International Conference on Semantic Computing, ICSC 2011 |
|---|---|
| 市 | Palo Alto, CA |
| 期間 | 11/9/18 → 11/9/21 |
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ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Science Applications
- Theoretical Computer Science
これを引用
Feature distance-based framework for classification of low-frequency semantic relations. / Horie, André Kenji; Ishizuka, Mitsuru.
Proceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011. 2011. p. 59-66 6061437.研究成果: Conference contribution
}
TY - GEN
T1 - Feature distance-based framework for classification of low-frequency semantic relations
AU - Horie, André Kenji
AU - Ishizuka, Mitsuru
PY - 2011
Y1 - 2011
N2 - In the relation extraction of semantic relations, it is not uncommon to face settings in which the training data provides very few instances of some relation classes. This is mostly due to the high cost of producing such data and to the class imbalance problem, which may result in some classes presenting small frequencies even with a large annotated corpus. This work thus presents a semi-supervised bootstrapped method to expand this initial training dataset, using pattern matching to extract new candidate instances from the Web. The core of this process uses a multiview feature distance-based framework, which allows quantitative and qualitative analysis of intermediate steps of the process. Experimental results show that this framework provides better results in the relation classification task than the baseline, and the bootstrapped architecture improves the relation classification task as a whole for these low-frequency semantic relations settings.
AB - In the relation extraction of semantic relations, it is not uncommon to face settings in which the training data provides very few instances of some relation classes. This is mostly due to the high cost of producing such data and to the class imbalance problem, which may result in some classes presenting small frequencies even with a large annotated corpus. This work thus presents a semi-supervised bootstrapped method to expand this initial training dataset, using pattern matching to extract new candidate instances from the Web. The core of this process uses a multiview feature distance-based framework, which allows quantitative and qualitative analysis of intermediate steps of the process. Experimental results show that this framework provides better results in the relation classification task than the baseline, and the bootstrapped architecture improves the relation classification task as a whole for these low-frequency semantic relations settings.
KW - Concept description
KW - Natural language text
KW - Semantic computing
UR - http://www.scopus.com/inward/record.url?scp=81255147433&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=81255147433&partnerID=8YFLogxK
U2 - 10.1109/ICSC.2011.9
DO - 10.1109/ICSC.2011.9
M3 - Conference contribution
AN - SCOPUS:81255147433
SN - 9780769544922
SP - 59
EP - 66
BT - Proceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011
ER -