{'fraction_non_alphanumeric': 0.016898608349900597, 'fraction_numerical': 0.0, 'mean_word_length': 5.496774193548387, '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}}
A survey of semantic similarity measuring techniques for information retrieval

The purpose of finding the Semantic Similarity is to find a degree of match between the aspects which are conceptually similar but not lexically. It is an important issue in the field of web information retrieval which requires retrieving a set of documents that are semantically related to a given query posed by the user. For the efficient information retrieval from the web that involves the collection of documents related to a domain, it is required to explore various facets of semantic similarity and design their respective quantitative metrics. These aspect are spread across various features like individual /Isolated words, terms (multi-word expressions/phrases/domain specific terms), sentences, entities, documents of the same language, documents of different languages. This paper takes into account the various similarity aspects explored in the literature and their respective possible quantification measures.