Publication Type |
Journal Article |
School or College |
College of Engineering |
Department |
Computing, School of |
Creator |
Riloff, Ellen M. |
Other Author |
Kozareva, Zornitsa; Hovy, Eduard |
Title |
Learning and evaluating the content and structure of a term taxonomy |
Date |
2009 |
Description |
In this paper, we describe a weakly supervised bootstrapping algorithm that reads Web texts and learns taxonomy terms. The bootstrapping algorithm starts with two seed words (a seed hypernym (Root concept) and a seed hyponym) that are inserted into a doubly anchored hyponym pattern. In alternating rounds, the algorithm learns new hyponym terms and new hypernym terms that are subordinate to the Root concept. We conducted an extensive evaluation with human annotators to evaluate the learned hyponym and hypernym terms for two categories: animals and people. |
Type |
Text |
Publisher |
Association for the Advancement of Artificial Intelligence (AAAI) |
First Page |
1 |
Last Page |
8 |
Subject |
Weakly supervised; Bootstrapping algorithm; Seed hypernym; Seed hyponym; Root concept; Term taxonomy; Learning by reading systems |
Subject LCSH |
Information retrieval |
Language |
eng |
Bibliographic Citation |
Kozareva, Z., Hovy, E., & Riloff, E. M. (2009). Learning and evaluating the content and structure of a term taxonomy. AAAI-09 Spring Symposium on Learning by Reading and Learning to Read, 1-8. |
Rights Management |
(c)AAAI http://www.aaai.org/ |
Format Medium |
application/pdf |
Format Extent |
119,774 bytes |
Identifier |
ir-main,12419 |
ARK |
ark:/87278/s65d991s |
Setname |
ir_uspace |
ID |
703626 |
Reference URL |
https://collections.lib.utah.edu/ark:/87278/s65d991s |