Word Substitution in Short Answer Extraction: A WordNet-based Approach
Qingqing Cai, James Gung, Maochen Guan, Gerald Kurlandski, Adam Pease
Abstract
We describe the implementation of a short answer extraction system. It consists of a simple sentence selection front-end and a two phase approach to answer extraction from a sentence. In the first phase sentence classification is performed with a classifier trained with the passive aggressive algorithm utilizing the UIUC dataset and taxonomy and a feature set including word vectors. This phase outperforms the current best published results on that dataset. In the second phase, a sieve algorithm consisting of a series of increasingly general extraction rules is applied, using WordNet to find word types aligned with the UIUC classifications determined in the first phase. Some very preliminary performance metrics are presented.- Anthology ID:
- 2016.gwc-1.11
- Volume:
- Proceedings of the 8th Global WordNet Conference (GWC)
- Month:
- 27--30 January
- Year:
- 2016
- Address:
- Bucharest, Romania
- Venue:
- GWC
- SIG:
- Publisher:
- Global Wordnet Association
- Note:
- Pages:
- 66–73
- Language:
- URL:
- https://aclanthology.org/2016.gwc-1.11
- DOI:
- Cite (ACL):
- Qingqing Cai, James Gung, Maochen Guan, Gerald Kurlandski, and Adam Pease. 2016. Word Substitution in Short Answer Extraction: A WordNet-based Approach. In Proceedings of the 8th Global WordNet Conference (GWC), pages 66–73, Bucharest, Romania. Global Wordnet Association.
- Cite (Informal):
- Word Substitution in Short Answer Extraction: A WordNet-based Approach (Cai et al., GWC 2016)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2016.gwc-1.11.pdf