Abstract
This paper surveys three research directions in parsing. First, we look at methods for both automatically generating a set of diverse parsers and combining the outputs of different parsers into a single parse. Next, we will discuss a parsing method known as transformation-based parsing. This method, though less accurate than the best current corpus-derived parsers, is able to parse quite accurately while learning only a small set of easily understood rules, as opposed to the many-megabyte parameter files learned by other techniques. Finally, we review a recent study exploring how people and machines compare at the task of creating a program to automatically annotate noun phrases.- Anthology ID:
- 2000.iwpt-1.2
- Volume:
- Proceedings of the Sixth International Workshop on Parsing Technologies
- Month:
- February 23-25
- Year:
- 2000
- Address:
- Trento, Italy
- Venue:
- IWPT
- SIG:
- SIGPARSE
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1–5
- Language:
- URL:
- https://aclanthology.org/2000.iwpt-1.2
- DOI:
- Cite (ACL):
- Eric Brill, John C. Henderson, and Grace Ngai. 2000. Automatic Grammar Induction: Combining, Reducing and Doing Nothing. In Proceedings of the Sixth International Workshop on Parsing Technologies, pages 1–5, Trento, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Automatic Grammar Induction: Combining, Reducing and Doing Nothing (Brill et al., IWPT 2000)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2000.iwpt-1.2.pdf