{'fraction_non_alphanumeric': 0.025096525096525095, 'fraction_numerical': 0.015444015444015444, 'mean_word_length': 5.246987951807229, '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}}
Lexical idiosyncrasy in MWE extraction

A wide scale of different NLP methods have been investigated for the extraction of Multiword Expressions from large corpora. While a good deal of recent research has been focusing on the development of reliable means to delineate different subclasses of MWEs with respect to the degree of their compositionality (Baldwin et al., 2003; McCarthy et al., 2003), it has been generally accepted that for the "simple" task of separating MWEs from fully productive word combinations, the substitutability of component words in a multiword unit with semantic neighbours could be a good indicative measure (Bannard et al., 2003). The underlying assumption is that MWEs do not generally tolerate the replacement of their components with semantically similar items. (Let us call this phenomenon lexical idiosyncrasy.) If we could represent this substitutability by some ranking measure, we will have reliable information whether a word combination could be considered a multiword unit or not (Pearce, 2001).