{'fraction_non_alphanumeric': 0.008997429305912597, 'fraction_numerical': 0.0, 'mean_word_length': 5.6581196581196584, '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}}
Domain specific information retrieval and text mining in medical document

This paper introduces a domain specific knowledge discovery technique that is applicable for both information retrieval and text mining, identifying word meanings characterized by domains. The meaning of words is identified by using a domain fusion algorithm that not only narrows domain concepts from different domain knowledge but also avoids the unknown domain problem so that specific domains can be found in a series of words. Domain knowledge is presented for the purpose of experiments on medical documents. Experiments performed over two different fields: query expansion in information retrieval and text classification in text mining, demonstrate the effectiveness of the proposed methodology.