The Reading Machine: A Versatile Framework for Studying Incremental Parsing Strategies

Franck Dary, Alexis Nasr


Abstract
The Reading Machine, is a parsing framework that takes as input raw text and performs six standard nlp tasks: tokenization, pos tagging, morphological analysis, lemmatization, dependency parsing and sentence segmentation. It is built upon Transition Based Parsing, and allows to implement a large number of parsing configurations, among which a fully incremental one. Three case studies are presented to highlight the versatility of the framework. The first one explores whether an incremental parser is able to take into account top-down dependencies (i.e. the influence of high level decisions on low level ones), the second compares the performances of an incremental and a pipe-line architecture and the third quantifies the impact of the right context on the predictions made by an incremental parser.
Anthology ID:
2021.iwpt-1.3
Volume:
Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | IWPT
SIG:
SIGPARSE
Publisher:
Association for Computational Linguistics
Note:
Pages:
26–37
Language:
URL:
https://aclanthology.org/2021.iwpt-1.3
DOI:
10.18653/v1/2021.iwpt-1.3
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.iwpt-1.3.pdf