{'fraction_non_alphanumeric': 0.029143897996357013, 'fraction_numerical': 0.01092896174863388, 'mean_word_length': 4.670103092783505, '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 combined approach for the extraction of the multi-word and nested biomedical entity

Name entity recognition is the fundamental task in text mining area. This work focuses on the problems of multi-word and nested entity names. A combined approach is proposed for identifying multi-word and nested bio-entity names, which achieve an F-measure of 80.8% in extracting the total of bio-entity names and an F-measure of 82.2% aiming at nested entities. Experimental results show the combined approach is promising for developing text mining technology.