In order to improve the early diagnosis rate of Parkinson's disease (PD), reduce the complications of PD in the later stage, and make the clinical intervention to alleviate the pain of Parkinson's patients early, Magnetic resonance imaging (MRI), positron emission tomography (PET), and computerized tomography (CT) were used to evaluate the characteristics of PD. A total of 34 patients diagnosed with PD admitted to Qilu Hospital of Shandong University from January 2017 to December 2018 were included in the research. According to the severity of the disease, the patients were divided into the initial group (IG) (22 cases) and the latter group (LG) (12 cases). MRI, PET, and CT were used to scan the patient's brain to obtain different scan images. The obtained image was processed to improve the image resolution. Brain MRI and CT images were registered. The processed MRI and PET images were segmented to obtain the corresponding caudate nucleus (CN) segmented images. The complete CN volume of patients was obtained from the pixel points and compared with the calculated value of Pen G's Multifunctional Operative Dissector (PMOD). The accuracy of the established multimodal magnetic resonance (MMR) technique in the diagnosis of PD was determined by manual cutting (MC). The rate of CN lesion was calculated according to the formula to determine the correlation between PD and CN lesion. The results showed that MRI, PET and CT could successfully obtain brain images of patients. After processing, the sharpness of the picture increased, which was easy to observe and provided guarantee for follow-up experiments. Brain MRI and CT image registration can register brain images in different modes to the same space. MRI image can segment the complete CN, while PET image can segment the intact CN. Compared with MC, the probability of CN calculated by the formula had a small error, which could be ignored. The error value was less than the calculated value of PMOD. The rate of CN lesion in the IG was greatly lower than that in the LG (P < 0.05), which showed that the MRI, PET and CT combined multimodal MRI can be used to diagnose PD with a high rate of quasi-discontinuity, which provides a new technique for clinical diagnosis.

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Modal verbs express modality, and modality is concerned with the status of the proposition that describes an event, it also expresses the opinion and attitude of a speaker toward the proposition of an utterance. Since modalities are directly related to the objective world, subjective world, and language use, they have been a hot topic of philosophers, logicians and linguists. Philosophers concern with the relations between the objective world and the true/false values of the modality; logicians are interested in the relations among the possibility, necessity and the objective world; and linguists pay attention to the modality category, sense category, function, recognition, and application of modal verbs. In recent years, the linguistic studies of modal verbs have extended from general linguistic studies to computational linguistic studies. Since modal verbs are a complex semantic system and they are often indeterminate in senses, they have been a tough issue in linguistic studies and have attracted great attention. To clarify the status of the previous linguistic studies of modal verbs and reveal the characteristics of the studies will be of great significance for the further study. Therefore, this article will focus on the review of the previous linguistic studies of English modal verbs and the data mining of the characteristics of the previous studies, and based on the summary of the previous studies, give suggestions for the further study of the English modal verbs.

Semantic merger, which is a phenomenon of semantic convergence of two meanings of a word in a certain context, is a kind of semantic indeterminacy in natural language, however, it brings trouble for natural language processing. Discovery of the features causing semantic mergers has been a significant but tough issue in natural language processing. Until now this issue has remained untouched. Therefore, in this article, this issue is studied. Based on a 1.8 million word English multi-genre corpus, taking English modal verb may as the target word, the contextual features causing semantic mergers between may(root possibility) and may(epistemic possibility) are investigated by an approach of attribute partial order diagram (APOD). First, the objects of may is categorized into 3 classes based on the idea of the three-way decision: may 1(root possibility), may 2(epistemic possibility) and may 3(merger), then the rules for word sense disambiguation (WSD) of the three classes are extracted, respectively, and a comparison is made among the rules for different classes, and finally the features causing semantic merger of may are discovered. The discovered knowledge provides valuable evidence for finding the semantic merger, the cause of the semantic merger and the solution of semantic mergers of may, and the proposed approach can also be use for other modal verbs, which may benefit the natural language processing of English modal verbs.