PyLFG (Python Library for Lexical Functional Grammar) is a new open-source project that aims to provide a comprehensive set of tools for working within the Lexical Functional Grammar (LFG) formalism, developed by ARS Linguistica. LFG is a syntactic theory that represents the structure of natural language using a combination of lexical and functional information. This project provides a set of classes and methods for representing and manipulating LFG structures, including lexical functional structures (f-structures) and c-structures.
PyLFG’s primary goal is to make LFG research more accessible to a wider community by providing a fully modular library that allows researchers to easily try new ideas and improve existing ones. The library will not only parse sentences but also generate sentences from f-structures, and it will have transfer capabilities to do automated translation.
PyLFG is also designed to be user-friendly, providing both a TUI (Terminal User Interface) and a WebUI for easy interaction. The library being written in Python makes it perfectly suited for linguistics students who are taught Python in university.
The lack of open-source LFG parsers is a significant problem in the field of linguistics research. Many academically developed LFG parsers have been abandoned by their authors, making them inaccessible to the wider community. This is a major obstacle for researchers who are interested in studying LFG, as they are often forced to rely on proprietary software that is difficult to access and modify.
The open-source nature of PyLFG is crucial in addressing this problem. By providing an open-source LFG parser, PyLFG aims to make LFG research more accessible to a wider community, allowing researchers to easily try new ideas and improve existing ones. The ability to view and modify the source code of the library also allows for greater transparency and reproducibility in research, which is essential for the advancement of knowledge.
Moreover, PyLFG’s open-source nature allows for collaboration and contributions from the wider community. This can lead to the development of new features and improvements in the library, making it a valuable tool for linguistics students and professors, language learning applications, and documenting under-resourced languages.
Open science is the best way of spreading and furthering knowledge, and PyLFG aims to contribute to this goal by providing a valuable tool that can be used by researchers and practitioners alike. The open-source nature of PyLFG will ensure that it is accessible to anyone who is interested in studying LFG, making it an essential tool for advancing knowledge in the field of linguistics.
PyLFG is still in early development and has not yet reached a stable release state.
PyLFG is designed to be a comprehensive tool for working within the Lexical Functional Grammar (LFG) formalism, providing a range of features that make it a valuable tool for researchers and practitioners alike.
- Modularity: PyLFG is fully modular, allowing researchers to easily replace all parts of the LFG parsing pipeline, from tokenizing sentences to the rich visualizations of the x-structures. This allows for flexibility in trying new ideas and improving existing ones, making it a valuable tool for advancing knowledge in the field of linguistics.
- Parsing and generation: PyLFG is not only able to parse sentences but also generate sentences from f-structures, which is an essential capability for automated translation and language learning applications.
- Transfer capabilities: PyLFG has transfer capabilities which allow it to do automated translation between languages, making it a useful tool for researchers and practitioners working on natural language processing.
- User-friendly interface: PyLFG provides both a TUI (Terminal User Interface) and a WebUI for easy interaction, making it accessible to a wide range of users, including students and professors who are taught Python in university.
- Open-source: PyLFG is an open-source project, meaning that the source code is freely available for anyone to view and modify. This allows for greater transparency and reproducibility in research, and also enables collaboration and contributions from the wider community.
- Suitable for linguistics students and professors: PyLFG is written in Python, which is a widely used programming language in the field of linguistics research. This makes it perfectly suited for linguistics students and professors, as well as python programmers in general.
- Valuable tool for documenting under-resourced languages: PyLFG provides a valuable tool for documenting under-resourced languages, which is essential for preserving and studying these languages.
In summary, PyLFG is a comprehensive and user-friendly open-source tool that provides a range of features that make it valuable for linguistics students and professors, language learning applications, and documenting under-resourced languages.
PyLFG can be used in a variety of applications, making it a valuable tool for researchers and practitioners working in a range of fields. Some of the key use cases include:
- Linguistics research: PyLFG is a valuable tool for linguistics students and professors who study syntactic theories. It can be used to parse sentences and generate f-structures, which can be used to test hypotheses and advance knowledge in the field.
- Language learning applications: PyLFG’s ability to generate sentences from f-structures makes it a useful tool for language learning applications. It can be used to generate grammatically correct sentences for language learners to practice, and its transfer capabilities can also be used to translate between languages.
- Documenting under-resourced languages: PyLFG’s ability to parse and generate sentences makes it a valuable tool for documenting under-resourced languages. It can be used to create grammatically correct sentences for use in language dictionaries and phrasebooks, and its transfer capabilities can also be used to translate between languages.
- Natural language processing: PyLFG’s transfer capabilities and ability to generate sentences from f-structures make it a valuable tool for researchers and practitioners working on natural language processing. It can be used to automate the translation of sentences between languages, which has a wide range of applications, such as machine translation and language-based search engines.
- Language-based search engines: PyLFG can be used to generate f-structures, which can be used to create grammatically correct sentences for use in language-based search engines. This makes it a valuable tool for researchers and practitioners working in this field.
In summary, PyLFG is a versatile tool that can be used in a wide range of applications, making it a valuable tool for researchers and practitioners working in linguistics, language learning, natural language processing, and language-based search engines.
PyLFG’s architecture is divided into five main components:
- Tokenization: PyLFG uses the NLTK library to tokenize sentences. This allows users to tokenize sentences in multiple languages, including English, Spanish, French, and German.
- Morphological Analysis: PyLFG uses a morphological analyzer to identify the base forms of words and their grammatical properties. This is an important step in the LFG parsing process, as it provides the necessary information for generating f-structures.
- Parsing: PyLFG uses a modified version of the C-Structure Earley parser to parse sentences and generate f-structures. This parser is based on the Earley algorithm, which is a top-down parsing algorithm that is well-suited for parsing context-free grammars.
- C-Structure and F-Structure Construction: PyLFG uses the information obtained from tokenization and morphological analysis to construct both C-structures and F-structures. These structures represent the syntactic and functional properties of sentences, respectively.
- Visualization: PyLFG uses the Graphviz library to visualize f-structures. This allows users to create rich visualizations of f-structures, making it easy to understand and analyze the structures generated by the parser.
In addition, PyLFG also provides a TUI (Terminal User Interface) and a WebUI (Web User Interface) which makes it easy for users to interact with the library, parse sentences and visualize the f-structures
PyLFG is written in Python, which means users of the library can leverage the wide range of libraries and frameworks available in Python, such as NLTK and Spacy, which makes it a powerful and flexible tool for working with LFG structures.
PyLFG is an open-source project and we welcome contributions from the community. There are several ways to contribute, including:
- Reporting bugs: If you find a bug in PyLFG, please report it on the GitHub issue tracker. Be sure to include as much information as possible, such as the version of PyLFG you are using, the steps to reproduce the bug, and any error messages you received.
- Suggesting features: If you have an idea for a new feature in PyLFG, please suggest it on the GitHub issue tracker. Be sure to include as much detail as possible about the feature, including any relevant use cases or examples.
- Writing code: If you would like to contribute code to PyLFG, please fork the repository on GitHub and submit a pull request. Be sure to follow the existing code style, and include tests and documentation for your changes.
- Documentation: If you find any errors or inconsistencies in the documentation, or would like to suggest improvements, please let us know on the GitHub issue tracker.
- Examples: If you have examples of using PyLFG in your research or projects, we would love to hear about them and feature them in the documentation.
We are looking forward to hearing from you and working together to improve PyLFG. Thank you for considering to contribute to this project!
Please also note that by contributing to this project, you agree to abide by our code of conduct.
PyLFG is a powerful open-source Python library for working within the Lexical Functional Grammar (LFG) formalism. It provides a comprehensive set of classes and methods for representing and manipulating LFG structures, including lexical functional structures (f-structures) and c-structures. With its modular design, PyLFG allows researchers to easily experiment with new ideas and customize their LFG parsing pipeline.
By making PyLFG open-source, we aim to contribute to the goal of open science and the spread and advancement of knowledge in the field of linguistics.
We invite the community to contribute to the development of PyLFG by reporting bugs, suggesting new features, writing code, improving documentation and sharing examples of using PyLFG. We are looking forward to working together to improve PyLFG and make it an even more powerful tool for researchers in the field of linguistics.
The package provides helper functions for loading grammar rules and
lexicon from files, and a LFGParseTree and LFGParseTreeNode
class for representing and visualizing parse trees. Here is an example
of how to use the package:
from pylfg import build_parse_trees
sentence = "the cat sits on the mat"
# load grammar and lexicon
grammar = load_grammar("path/to/grammar.txt")
lexicon = load_lexicon("path/to/lexicon.txt")
# parse sentence
trees = build_parse_trees(sentence, grammar, lexicon)
# print the first parse tree
print(trees[0])You can also use the LFGParseTree.to_f_structure method to export the f-structure of the sentence in latex format, as shown in this example
f_structure = trees[0].to_f_structure()
# write f-structure to a latex file
with open("f_structure.tex", "w") as f:
f.write(f_structure)