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In the traditional text similarity calculation, most of the TF-IDF method. TF-IDF establishes the word frequency vector for the text, and calculates the cosine between the vectors as the similarity of the text. The algorithm is widely used in many search engines, information retrieval system can be seen, but in the text of the vocabulary processing is not ideal. The synonyms between professional phrases are not perceived by models, and they are used as different words to calculate similarity. In this paper, synonymous with biomedical field as an example, in the TF-IDF model embedded synonyms recognition function. Firstly, this method acquires the synonyms of the vocabulary in the biomedical field and establishes the synonyms, then identifies the synonyms in the TF-IDF model and calculates the better weight of the phrase. The experimental results show that this method can effectively improve the precision of text similarity calculation in biomedical field, and it is a more effective than the traditional TF-IDF text similarity calculation method. INTRODUCTION With the rapid growth of information resources, users are more concerned about how to find the information from the massive information resources. The similarity calculation of the text plays an important role in this field and has a wide range of applications in many fields: in the field of information retrieval, text similarity calculation is considered to be one of the best ways to improve the retrieval effect .Text similarity calculation is commonly used TF-IDF (Term Frequency & Inverse Documentation Frequency) method. It uses the frequency frequency TF (Term Frequency) and the inverse document frequency IDF (Inverse Document Frequency) as the document feature value. The text is modeled as a word frequency vector based on the Vector Space Model (VSM). And then calculate the vector cosine as the similarity between the text . Now the arrival of large data age, making a large number of text data from the more accurate access to information has become an important issue of scientific research. We must make full use of the text before the different terms of the relationship, not just the frequency of each word appears. One of the obvious relationships is the synonyms. So, this paper presents a method for calculating the similarity of TF-IDF texts for synonyms in biomedical domains, and experiments on the proteomic experimental metadata set. The experimental results show that the method can improve the accuracy of recognition. This method uses the text of the biological field to construct the text library, and combines the synonyms in the vocabulary to establish the synonyms based on the field of biomedical ontology. First identify the synonyms in the text and treat them as the same term. and then calculate the text similarity based on TF-IDF method. This method avoids important information that is lost when using different biomedical phrases in the text to express the same meaning. Thereby improving the accuracy in the biomedical field text similarity. 578 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017) Advances in Engineering Research (AER), volume 130

A Method for Calculating the Similarity of TF - IDF Texts for Synonyms in Biomedical Domains