FASTUS: A Finite-State Processor for Information Extraction from Real-World Text
Abstract
Approaches to text processing that rely on parsing the text with a context-free grammar tend to be slow and error-prone because of the massive ambiguity of long sentences. In contrast, FASTUS employs a nondeterministic finite-state language model that produces a phrasal decomposition of a sentence into noun groups, verb groups and particles. Another finite-state machine recognizes domain-specific phrases based on combinations of the heads of the constituents found in the first pass. FASTUS has been evaluated on several blind tests that demonstrate that state-of-the-art performance on information-extraction tasks is obtainable with surprisingly little computational effort.
Cite
Text
Appelt et al. "FASTUS: A Finite-State Processor for Information Extraction from Real-World Text." International Joint Conference on Artificial Intelligence, 1993.Markdown
[Appelt et al. "FASTUS: A Finite-State Processor for Information Extraction from Real-World Text." International Joint Conference on Artificial Intelligence, 1993.](https://mlanthology.org/ijcai/1993/appelt1993ijcai-fastus/)BibTeX
@inproceedings{appelt1993ijcai-fastus,
title = {{FASTUS: A Finite-State Processor for Information Extraction from Real-World Text}},
author = {Appelt, Douglas E. and Hobbs, Jerry R. and Bear, John and Israel, David J. and Tyson, Mabry},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {1993},
pages = {1172-1178},
url = {https://mlanthology.org/ijcai/1993/appelt1993ijcai-fastus/}
}