{'fraction_non_alphanumeric': 0.016304347826086956, 'fraction_numerical': 0.0, 'mean_word_length': 5.395833333333333, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Ambiguity in context-free grammars

This paper presents a solution to the CFG Ambiguity Problem which has come about in a recent automatic language acquisition research. The ambiguity problem of a grammar has not been addressed in prior language acquisition research. A theorem has told us to classify the CFG ambiguity problem as undecidable. Many researchers hence simply ignore the ambiguity problem or generate only unambiguous grammar. However, grammatical ambiguity lurks behind every language acquisition scheme. An ambiguity is undesirable because different derivation trees represent different meanings. This paper has investigated the CFG ambiguity problem. There are two significant results: First, the problem is solvable in an application in which the string length is bounded, that is most applications. Second, the ambiguity search algorithm developed can solve the PCP problem with bounded string length.