Semantic Similarity Calculation Method Research Articles

The Sharing Model of Kindergarten Art Enlightenment Education Curriculum Resources Based on Deep Learning Algorithm

In order to meet the autonomous needs of children’s art learning, this paper studies the sharing model of kindergarten art enlightenment education curriculum resources under the deep learning algorithm. Build a kindergarten art enlightenment education curriculum resource-sharing model that includes the classification and storage layer of education curriculum resources, the metadata annotation layer of education curriculum resources and the shared business service layer. The deep learning algorithm is used in the classification and storage layer of education curriculum resources. The education curriculum resources are divided into courseware resources, case resources and media materials resources through unsupervised restricted Boltzmann machine (RBM) coding and supervised training process. The ontology model of various educational curriculum resources is constructed, and the semantic information of educational curriculum resources is described using ontology. The metadata annotation layer of education curriculum resources annotates the metadata semantics of education curriculum resources through the constructed ontology, which improves the consistency and sharing of education curriculum resources management. The implementation of resource sharing business service is based on semantic analysis and query of educational curriculum resources. Therefore, the shared business service layer adopts a semantic query method based on improved similarity algorithm, which combines semantic distance, node density, node depth and relationship type to build a semantic similarity calculation method to achieve semantic analysis and query of educational curriculum resources. The experimental results show that the model is suitable for multi-classification of shared resources in the kindergarten art enlightenment education curriculum, and the semantic query accuracy is high in the process of sharing. When the threshold is [Formula: see text] 0.5, the query effect of the sharing model in this paper is ideal, which can meet the needs of users’ autonomous learning.

Chinese Natural Language Processing: From Text Categorization to Machine Translation

Abstract The level and volume of automatic computerized processing of linguistic information has become one of the most important criteria for measuring whether a country has entered the information society. The study begins with statistical linguistics and aims to process complicated Chinese information. In this paper, after establishing the word database of the Chinese language, the language model is smoothed and compressed, the Chinese character information and Chinese language information are extracted, and the processing of Chinese grammar and Chinese semantic information is emphasized. Among them, Chinese grammar processing includes Chinese word analysis and basic phrase analysis based on the maximum entropy model, and Chinese semantic processing includes Bayesian-based word sense disambiguation, semantic role labeling based on the conditional random field model, and thesaurus-based semantic similarity calculation method. In addition, SECTILE-based Chinese text categorization and statistical linguistics-based machine translation methods are explored and tested for their effectiveness in Chinese natural language processing. The results show that the total average check accuracy and check the completeness of Chinese text are 78.65% and 72.24%, respectively, and the BLEU values of the translation methods are improved by [1.62,3.73] and [0.93,5.01] compared with the Baseline method, which is able to process the Chinese information accurately. The research plays an important role in the process of information processing based on Chinese language processing.

Relational Analysis of College English Vocabulary - A Reflection Based on Semantic Association Network Modeling

Abstract Vocabulary is an essential and crucial part of college English learning. However, the lack of knowledge about the intrinsic connection between words has become a significant obstacle to vocabulary learning and teaching. In this study, we use a semantic association network to construct a relationship model between English words and introduce a tree kernel function into the syntactic analyzer to extract relationships between English words. The dictionary-based lexical semantic similarity calculation method is combined with a corpus-based English lexical semantic similarity calculation method for the extracted relations. Furthermore, the study creates datasets that relate to English vocabulary for simulation experiments. The results show that the more common sense phenomena there are, the higher the correlation value is. Furthermore, the similarity of university English vocabulary has a high degree of correlation with the type of specialized elements. The overall performance of the lexicon is ranked as synonym forest (0.431ρ/0.479r) > HowNet (0.249ρ/0.341r) > English WordNet (−0.051ρ/−0.003r), and the synonym forest method has the best performance, with the highest accuracy of 85.57% in classifying different lexical relations. The study offers valuable references and lessons for related research in natural language processing.

An Ensemble Semantic Textual Similarity Measure Based on Multiple Evidences for Biomedical Documents.

With the increasing volume of the published biomedical literature, the fast and effective retrieval of the literature on the sequence, structure, and function of biological entities is an essential task for the rapid development of biology and medicine. To capture the semantic information in biomedical literature more effectively when biomedical documents are clustered, we propose a new multi-evidence-based semantic text similarity calculation method. Two semantic similarities and one content similarity are used, in which two semantic similarities include MeSH-based semantic similarity and word embedding-based semantic similarity. To fuse three different similarities more effectively, after, respectively, calculating two semantic and one content similarities between biomedical documents, feedforward neural network is applied to integrate the two semantic similarities. Finally, weighted linear combination method is used to integrate the semantic and content similarities. To evaluate the effectiveness, the proposed method is compared with the existing basic methods, and the proposed method outperforms the existing related methods. Based on the proven results of this study, this method can be used not only in actual biological or medical experiments such as protein sequence or function analysis but also in biological and medical research fields, which will help to provide, use, and understand thematically consistent documents.

Text Similarity Measurement Method and Application of Online Medical Community Based on Density Peak Clustering

Text similarity measurement is a link between basic research such as text modeling and upper-level application research of text potential information. In order to improve the accuracy of text similarity measurement, this paper proposes a semantic similarity calculation method integrating word2vec model and TF-IDF, and applies it to the density peak clustering of Chinese text data consulted by patients in online medical community. Experimental results show that the proposed similarity measurement method is superior to the traditional method. Furthermore, the study is among the first to apply the density peak clustering algorithm to online medical community, which offers a reference for how to find out user demands from medical text data in the big data environment.

Knowledge Discovery-Based Analysis of Health Factors of Urinary Infections in Elderly Cardiology Inpatients.

A set of semantic similarity calculation methods combining full-text text and domain knowledge topics is proposed for the current study of entity association relations such as disease–gene in medical texts combined with topics in knowledge discovery, which is insufficient to reveal the deep semantic association relations of medical domain knowledge at topic level. Taking urinary infections in elderly inpatients as the research subject, word embedding representation of word vectors and topic vectors is performed by the TWE model, and similarity calculation is performed by combining text and domain knowledge topics based on Siamese Network framework. The urinary microbiological culture results of both groups were dominated by Escherichia coli, accounting for 34.65% and 47.92%, respectively; the use of antimicrobial drugs in the symptomatic urinary infection group was 94.19% higher than that in the asymptomatic bacteriuria group, 77.27% (x2 = 8.158, P=0.004).

Research on semantic similarity calculation methods in Chinese financial intelligent customer service

Research on semantic similarity calculation methods in Chinese financial intelligent customer service

Research on semantic similarity calculation methods in Chinese financial intelligent customer service

Research on semantic similarity calculation methods in Chinese financial intelligent customer service

Automatic Translation of Medical Information Based on Text Feature Recognition

In order to improve the effect of the medical information automatic translation system, based on the text feature recognition technology of medical image, this paper constructs an automatic translation system that can recognize medical images. On the basis of the medical information image retrieval based on the combination of text information and visual information, this paper uses automatic image annotation technology and semantic similarity calculation method to extract the text and semantic features of medical information images. Then, this paper uses the inherent multi-information fusion capability of the Bayesian inference network to fuse the text features of medical information images and the semantic features of image content together to realize medical information image retrieval. Finally, this paper designs experiments to test the performance of the medical information automatic translation system. The research shows that the system constructed in this paper has certain effects.

Research on Semantic Similarity of Short Text Based on Bert and Time Warping Distance

The research on semantic similarity of short text plays an important role in machine translation, emotion analysis, information retrieval and other AI business applications. However, according to existing short text similarity research, the characteristics of ambiguous vocabularies are difficult to be effectively analyzed, the solution of the problem caused by words order needs to be further optimized as well. This paper proposes a short text semantic similarity calculation method that combines BERT and time warping distance algorithm, in order to solve the problem of vocabulary ambiguity. The model first uses the pre trained Bert model to extract the semantic features of the short text from the whole level, and obtains a 768 dimensional short text feature vector. Then, it transforms the extracted feature vector into a point sequence in space, uses the CTW algorithm to calculate the time warping distance between the curves connected by the point sequence, and finally uses the weight function designed by the analysis, according to the smaller the time warpage distance is, the higher the degree of small similarity is, to calculate the similarity between short texts. The experimental results show that this model can mine the feature information of ambiguous words, and calculate the similarity of short texts with lexical ambiguity effectively. Compared with other models, it can distinguish the semantic features of ambiguous words more accurately.

Scene Understanding Technology of Intelligent Customer Service Robot Based on Deep Learning

As a value-added service that improves the efficiency of online customer service, customer service robots have been well received by sellers in recent years. Because the robot strives to free the customer service staff from the heavy consulting services in the past, thereby reducing the seller’s operating costs and improving the quality of online services. The purpose of this article is to study the intelligent customer service robot scene understanding technology based on deep learning. It mainly introduces some commonly used models and training methods of deep learning and the application fields of deep learning. Analyzed the problems of the traditional Encoder-Decoder framework, and introduced the chat model designed in this paper based on these problems, that is, the intelligent chat robot model (T-DLLModel) obtained by combining the neural network topic model and the deep learning language model. Conduct an independent question understanding experiment based on question retelling and a question understanding experiment combined with contextual information on the dialogue between online shopping customer service and customers. The experimental results show that when the similarity threshold is 0.4, the method achieves better results, and an F value of 0.5 is achieved. The semantic similarity calculation method proposed in this paper is better than the traditional method based on keywords and semantic information, especially when the similarity threshold increases, the recall rate of this paper is significantly better than the traditional method. The method in this article has a slightly better answer sorting effect on the real customer service dialogue data than the method based on LDA.

Accurate Estimation of English Word Similarity Based on Semantic Network

The application of artificial intelligence in the field of English needs to process a large amount of English text data, but the deviation of English word similarity reduces its overall English translation accuracy and data processing efficiency. Therefore, this paper proposes an accurate estimation of English word similarity based on semantic network, which combines a variety of computing methods to form a compound computing structure based on semantic network. The experimental results show that the error between the Semantic Web-based English word similarity calculation method and manual evaluation is small, and the accuracy of English word similarity calculation is improved to a certain extent. In addition, compared with other English word similarity calculation methods, the English word similarity calculation method based on semantic network is more in line with people’s cognition and understanding of knowledge, has higher reliability, and has certain practical value in the field of English.

Improved Chinese Sentence Semantic Similarity Calculation Method Based on Multi-Feature Fusion

In this paper, an improved long short-term memory (LSTM)-based deep neural network structure is proposed for learning variable-length Chinese sentence semantic similarities. Siamese LSTM, a sequence-insensitive deep neural network model, has a limited ability to capture the semantics of natural language because it has difficulty explaining semantic differences based on the differences in syntactic structures or word order in a sentence. Therefore, the proposed model integrates the syntactic component features of the words in the sentence into a word vector representation layer to express the syntactic structure information of the sentence and the interdependence between words. Moreover, a relative position embedding layer is introduced into the model, and the relative position of the words in the sentence is mapped to a high-dimensional space to capture the local position information of the words. With this model, a parallel structure is used to map two sentences into the same high-dimensional space to obtain a fixed-length sentence vector representation. After aggregation, the sentence similarity is computed in the output layer. Experiments with Chinese sentences show that the model can achieve good results in the calculation of the semantic similarity.

Software similarity measurements using UML diagrams: A systematic literature review

Every piece of software uses a model to derive its operational, auxiliary, and functional procedures. Unified Modeling Language (UML) is a standard displaying language for determining, recording, and building a software product. Several algorithms have been used by researchers to measure similarities between UML artifacts. However, there no literature studies have considered measurements of UML diagram similarities. This paper presents the results of a systematic literature review concerning similarity measurements between the UML diagrams of different software products. The study reviews and identifies similarity measurements of UML artifacts, with class diagram, sequence diagram, statechart diagram, and use case diagram being UML diagrams that are widely used as research objects for measuring similarity. Measuring similarity enables resolution of the problem domains of software reuse, similarity measurement, and clone detection. The instruments used to measure similarity are semantic and structural similarity. The findings indicate opportunities for future research regarding calculating other UML diagrams, compiling calculation information for each diagram, adapting semantic and structural similarity calculation methods, determining the best weight for each item in the diagram, testing novel proposed methods, and building or finding good datasets for use as testing material.

Semantic-based Compound Keyword Search over Encrypted Cloud Data

Keyword search over encrypted data is essential for accessing outsourced sensitive data in cloud computing. In some circumstances, the keywords that the user searches on are only semantically related to the data rather than via an exact or fuzzy match. Hence, semantic-based keyword search over encrypted cloud data becomes of paramount importance. However, existing schemes usually depend upon a global dictionary, which not only affects the accuracy of search results but also causes inefficiency in data updating. Additionally, although compound keyword search is common in practice, the existing approaches only process them as single words, which split the original semantics and achieve low accuracy. To address these limitations, we initially propose a compound concept semantic similarity (CCSS) calculation method to measure the semantic similarity between compound concepts. Next, by integrating CCSS with Locality-Sensitive Hashing function and the secure kk<; inline-graphic xlink:href="wang-ieq1-2847318.gif"/>-Nearest Neighbor scheme, a semantic-based compound keyword search (SCKS) scheme is proposed. SCKS achieves not only semantic-based search but also multi-keyword search and ranked keyword search. Additionally, SCKS also eliminates the predefined global library and can efficiently support data update. The experimental results on real-world dataset indicate that SCKS introduces low overhead on computation and the search accuracy outperforms the existing schemes.

Integration of anatomy ontology data with protein\u2013protein interaction networks improves the candidate gene prediction accuracy for anatomical entities

BackgroundIdentification of genes responsible for anatomical entities is a major requirement in many fields including developmental biology, medicine, and agriculture. Current wet lab techniques used for this purpose, such as gene knockout, are high in resource and time consumption. Protein–protein interaction (PPI) networks are frequently used to predict disease genes for humans and gene candidates for molecular functions, but they are rarely used to predict genes for anatomical entities. Moreover, PPI networks suffer from network quality issues, which can be a limitation for their usage in predicting candidate genes. Therefore, we developed an integrative framework to improve the candidate gene prediction accuracy for anatomical entities by combining existing experimental knowledge about gene-anatomical entity relationships with PPI networks using anatomy ontology annotations. We hypothesized that this integration improves the quality of the PPI networks by reducing the number of false positive and false negative interactions and is better optimized to predict candidate genes for anatomical entities. We used existing Uberon anatomical entity annotations for zebrafish and mouse genes to construct gene networks by calculating semantic similarity between the genes. These anatomy-based gene networks were semantic networks, as they were constructed based on the anatomy ontology annotations that were obtained from the experimental data in the literature. We integrated these anatomy-based gene networks with mouse and zebrafish PPI networks retrieved from the STRING database and compared the performance of their network-based candidate gene predictions.ResultsAccording to evaluations of candidate gene prediction performance tested under four different semantic similarity calculation methods (Lin, Resnik, Schlicker, and Wang), the integrated networks, which were semantically improved PPI networks, showed better performances by having higher area under the curve values for receiver operating characteristic and precision-recall curves than PPI networks for both zebrafish and mouse.ConclusionIntegration of existing experimental knowledge about gene-anatomical entity relationships with PPI networks via anatomy ontology improved the candidate gene prediction accuracy and optimized them for predicting candidate genes for anatomical entities.

UFO: A tool for unifying biomedical ontology-based semantic similarity calculation, enrichment analysis and visualization.

BackgroundBiomedical ontologies have been growing quickly and proven to be useful in many biomedical applications. Important applications of those data include estimating the functional similarity between ontology terms and between annotated biomedical entities, analyzing enrichment for a set of biomedical entities. Many semantic similarity calculation and enrichment analysis methods have been proposed for such applications. Also, a number of tools implementing the methods have been developed on different platforms. However, these tools have implemented a small number of the semantic similarity calculation and enrichment analysis methods for a certain type of biomedical ontology. Note that the methods can be applied to all types of biomedical ontologies. More importantly, each method can be dominant in different applications; thus, users have more choice with more number of methods implemented in tools. Also, more functions would facilitate their task with ontology.ResultsIn this study, we developed a Cytoscape app, named UFO, which unifies most of the semantic similarity measures for between-term and between-entity similarity calculation for all types of biomedical ontologies in OBO format. Based on the similarity calculation, UFO can calculate the similarity between two sets of entities and weigh imported entity networks as well as generate functional similarity networks. Besides, it can perform enrichment analysis of a set of entities by different methods. Moreover, UFO can visualize structural relationships between ontology terms, annotating relationships between entities and terms, and functional similarity between entities. Finally, we demonstrated the ability of UFO through some case studies on finding the best semantic similarity measures for assessing the similarity between human disease phenotypes, constructing biomedical entity functional similarity networks for predicting disease-associated biomarkers, and performing enrichment analysis on a set of similar phenotypes.ConclusionsTaken together, UFO is expected to be a tool where biomedical ontologies can be exploited for various biomedical applications.AvailabilityUFO is distributed as a Cytoscape app, and can be downloaded freely at Cytoscape App (http://apps.cytoscape.org/apps/ufo) for non-commercial use

Design of interactive intelligent assistant translation platform based on multi-strategies

At present, the intelligent translation of computer is developing rapidly. In this paper, the overall design of interactive intelligent auxiliary translation platform based on multi-strategy is studied. In this paper, the algorithm of similarity of sentences is deeply studied. According to the needs of English–Chinese translation memory system, the similarity between sentences is calculated from the sentence syntax and semantic based on syntactic and semantic English sentence similarity calculation method. Finally, the author uses the English–Chinese translation of Navigator 7.0 manual to examine the effect of the algorithm platform developed in this paper. The experimental results show that the proposed algorithm improves the speed of translation by about 35%.

Research on Tibetan Text Orientation Identification

In recent years, Minority languages in China are widely used on the computer and network. But now there is no effective public opinion analysis system of the minorities overall attitude of the masses of the hot events or topics. In this study, we research on Tibetan topic orientation recognition. First, according to the Tibetan context and life characteristics, combined with a set of emotional words in Hownet, the Tibetan emotional word dictionary is built, and then by the Tibetan word semantic similarity calculation method we extend this dictionary to get rich emotional word set. We also propose a method that the sentence orientation is determined by the orientation of words in this sentence and the orientation of text is determined by the orientation of sentences in this text. By our research the Tibetan hotspot information can be rapidly detected and found and then the public opinion tend can be track quickly. It is benefit for positive guidance of public opinion. ethod is intuitively to be implemented and very convenient to produce visually interesting results.

Ontology-based Dynamic and Semantic Similarity Calculation Method for Case-based Reasoning

In this paper, we propose an Ontology-based DYnamic and Semantic similaritY computation method for CBR (ODYSEY). ODYSEY is developed for the computation of dynamic similarity as well as semantic using dynamically changed ontology structure according to occurred context using CBR. Context is defined as any information that can be used to characterize the occurrence situation of a new problem and cases. To compute dynamic and semantic similarity, ontology is restructured by the context. The domain ontology is developed by consideration of contexts, and partial ontologies are extracted from the ontology including a set of shared contexts with same features and values between a new problem and cases. We implemented a mobile e-mentoring system, named MintStory© to show the applicability and feasibility of ODYSEY. As experimental results show, the satisfaction value of similarity of MintStory© is higher than the coordinator-recommended result. It shows that the ODYSEY is significantly efficient in a similarity ...