ChartParser provides a basic framework and simple user interface for chart parsing sentences as set forth by Kay (1982) and described by Jurafsky (2009). The user can import existing grammars and lexica (an example of each is provided in chartparser/data/), or create them on the fly.
This limited parser tokenizes words on whitespace and does not strip punctuation from the inputted sentence. Only one parse, if any, is provided to the user.
The format of the grammar and lexicon is very specific. Any deviation from this format may cause undesirable or no parses.
For this parser, the parent node "S" represents a sentence.
For more information about the mechanism of chart parsing, please see the original 2012 write-up for this code in docs/, where the original 2012 code (Appendix.txt) can also be found.
Any imported grammar must use the following format to specify a rule, one rule per line:
parent --> child1 child2 ... childn
The rule must have one parent on the left side of the arrow '-->'. The rule must have one or more children on the righthand side separated by spaces.
Any imported lexicon must use the following format to specify a lexical entry, one entry per line:
word : part_of_speech
The lexical entry must have exactly one word on the left hand side (no compound words unless they are joined by a character other than whitespace, which the user must expect to also provide in the inputted sentence) and one part of speech on the left hand side.
- Python3
- tkinter
Tests can be run by calling pytest from anywhere in the package. Tests only evaluate the functionality of the parser and its component parts - the GUI is not evaluated.
ChartParser 0.2.0 - Current parser, upgraded to use object oriented programming, but otherwise same funcationlity as version 0.1.0. ChartParser 0.1.0 - First GUI chart parser built for CSE 415 class in Spring 2012.
Kathryn Egan
TBD
Thanks to Maria McKinley and Chris Barker at UW for reviewing version 1.1.0 of this code, and to Professor Tanimoto for reviewing version 0.1.0.
Jurafsky, D. and Martin J (2009). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. 2nd ed. Upper Saddle River, New Jersy: Pearson/Prentice-Hall.
Kay, M. (1982). Algorithm schemata and data structures in syntactic processing. In Allen, S. (Ed.) Text Processing: Text Analysis and Generation, Text Typology and Attribution, pp. 327-358. Almqvist and Wiksell, Stockholm.