The techniques used in this paper can be extended to handle other formal languages beyond palindromes and squares by adapting the algorithms to suit the specific properties and structures of those languages. Here are some ways to extend the techniques:
Regular Languages: For regular languages, deterministic finite automata (DFA) can be used to represent the language, and the algorithms can be modified to work with DFA transitions and states.
Context-Free Languages: For context-free languages, techniques from parsing algorithms like CYK or Earley's algorithm can be incorporated to handle the language distance problem efficiently.
Regular Expressions: The algorithms can be adapted to work with regular expressions, allowing for pattern matching and approximate matching with complex patterns.
Natural Language Processing: Extending the techniques to handle natural language processing tasks like sentiment analysis, named entity recognition, or part-of-speech tagging can provide valuable insights into text analysis and understanding.
Graph Languages: For languages represented as graphs, graph algorithms and traversal techniques can be utilized to compute language distances and similarities.