Keyword Extraction Algorithm Research Articles

A Graph-Based Keyword Extraction Method for Academic Literature Knowledge Graph Construction

In this paper, we construct an academic literature knowledge graph based on the relationship between documents to facilitate the storage and research of academic literature data. Keywords are an important type of node in the knowledge graph. To solve the problem that there are no keywords in some documents for several reasons in the process of knowledge graph construction, an improved keyword extraction algorithm called TP-CoGlo-TextRank is proposed by using word frequency, position, word co-occurrence frequency, and a word embedding model. By combining the word frequency and position in the document, the importance of words is distinguished. By introducing the GloVe word-embedding model, which brings the external knowledge of documents into the TextRank algorithm, and combining the internal word co-occurrence frequency in the documents, the word-adjacency relationship is transferred non-uniformly. Finally, the words with the highest scores are combined into phrases if they are adjacent in the original text. The validity of the TP-CoGlo-TextRank algorithm is verified by experiments. On this basis, the Neo4j graph database is used to store and display the academic literature knowledge graph, to provide data support for research tasks such as text clustering, automatic summarization, and question-answering systems.

A reversible natural language watermarking for sensitive information protection

Existing methods have evolved from using synonym substitution to incorporating arbitrary word substitution to achieve reversible natural language watermarking. However, a notable limitation is that they are prone to overlook the sensitivity of information associated with the original words, with a tendency to prefer non-sensitive words for substitution. As a result, a potential risk of sensitive information leakage contained in the original text is posed. Furthermore, while aiming for reversibility, the overall performance of the watermarking method may be inadvertently compromised. In response to the above problems, this paper puts forward a novel reversible natural language watermarking method that combines a Keyword Substitution scheme and a Prediction Error Expansion algorithm (KSPEE) to protect sensitive information, verify content integrity, protect copyright, and so on. Specifically, KSPEE leverages a keyword extraction algorithm to identify important content containing sensitive information in the original text, thereby determining the potential positions for watermark information embedding. Subsequently, a masked language model is utilized to predict appropriate substitution words based on the surrounding semantic information of the embedding position. In addition, the prediction error expansion algorithm is employed to select appropriate words for substituting the original keywords, ensuring the successful embedding of watermark information while maintaining the recoverability of the original keywords. By identifying keywords and substituting them, a suitable method of protecting the original sensitive information is provided. Extensive experiments demonstrate that, under the promise of semantic distortion and lossless restoration of the original content, the proposed method KSPEE achieves outstanding watermarked text quality. A higher watermark embedding rate is achieved and strong security is shown by KSPEE. More importantly, KSPEE effectively prevents the leakage of sensitive information.

An Accuracy Study of Personalized Recommendation System for E-commerce Based on Big Data Analysis

Abstract E-commerce, as an emerging value chain model for the global economy, has greatly promoted development, while the impact of digitalization on traditional publishing enterprises is increasingly evident. In this paper, we propose a TextRank keyword extraction algorithm based on comprehensive weights, which extracts and assigns keywords that identify user information, behavior, and product characteristics. We then output a keyword weight table for user information, user behavior, and product keywords. Finally, utilizing an optimized collaborative filtering recommendation algorithm, we establish a recommendation model between the user-commodity matrix to build an e-commerce personalized recommendation system that provides users with more accurate customized recommendations. The practical application of the designed personalized recommendation system is examined to evaluate its accuracy. The MAE of this algorithm is smaller than that of user-based (0.8915, 0.9470) or item-based (0.8873, 0.9327) collaborative filtering algorithms, indicating that the improved collaborative filtering algorithm effectively enhances system recommendation accuracy. The direct effect value of recommendation strength is 0.344, with an indirect effect value of 0.018, leading to the highest overall effect value. This study provides users with convenient and attentive services, significantly enhances user experience quality, and generates substantial profits for the e-commerce platform.

Development system for coordination of activities of experts in the formation of machineschetable standards in the field of military and space activities based on ontological engineering: a case study

Standardization and technical regulation involve the digitization of international and regional standards into "smart" standards, introducing new requirements for the presentation of complex industrial manuals. The introduction of artificial intelligence and machine learning into the process of standardization, improving process automation, data analysis will provide an opportunity to create innovative standards. Creation of machine-readable standards and their presentation in the form of a database will contribute to better interoperability between systems. The article demonstrates the method of ontological engineering for the automation of partial methodological examination of documents in accordance with the National Standardization Plan. As part of the experiment, an independent tool was developed, based on the extraction of qualitative data, using an uncontrolled automatic keyword extraction algorithm. The algorithm does not require the creation of a learning corpus, can be applied to any text and language, and does not have limitations on the amount of processed data. The algorithm provides adjustment of the parameters of the repetition of keywords, with the possibility of taking into account less important keywords. The work presents the following components: model classification standards, essence extraction module, categorization, thesaurus formation, monitoring and analysis. Calculations were carried out using formalization, determination of the target function, establishment of similarity measures, and description of document features. The work presents the theoretical aspect of application, design and description of the functional system.

Natural language processing (NLP) aided qualitative method in health research

Qualitative data analysis is produced frequently in healthcare settings, which is a time-consuming and skilled analytic task. The use of qualitative research findings in clinical settings takes years, which is sometimes obsolete knowledge as the health context is dynamic. Artificial Intelligence (AI)-based qualitative data analysis might present with rapid analysis of text-based data in real-time, thereby empowering qualitative researchers to expedite their analysis and facilitate timely use of the research findings. We tested an AI-based method to complement the manual analysis of text-based data from the verbatim transcripts of seven mall managers’ interviews. First, we prepared text data into a machine-calculable format and employed BERT model to extract sentence-level features in our case. Second, we implement TF-IDF-based keywords mining techniques to extract the main candidate themes from the interview transcripts to support text-based analysis, including: 1) primary cluster detection algorithm, and 2) keyword extraction algorithm. The extracted core themes provide qualitative researchers with a more comprehensive overview of the qualitative data. Most of the sentences clustered in meaningful short topics or sentences carrying independent and clear information. The extracted topics and clustered sentences reduced qualitative researchers’ workload by condensing and identifying meaningful concepts and naming them. This method combining contextualized word embeddings, unsupervised clustering, and keyword extraction techniques can significantly reduce the overall workload and time consumed in qualitative research using conventional methods.

Keyword Extraction-based Library Intelligence Services: Challenges, Adaptations and Reinvention

Abstract This paper proposes a domain knowledge base construction platform for thematic intelligence services of library intelligence institutions. The platform proposes a keyword extraction algorithm applicable to the literature resources of library intelligence services based on a statistical model and N-Gram model combined with feature word information. The automatic summary algorithm based on statistics combines the information of sentence content and location to comprehensively evaluate the weights of candidate summary sentences and achieve automatic summary extraction. Based on the HDP topic model, a literature recommendation model oriented to the resource retrieval context is proposed, which can convert the literature feature similarity calculation to the topic similarity calculation with implied literature feature semantics from the perspective of semantic analysis and improve the accuracy of recommended literature. The technical feasibility of this system platform is verified through relevant data experiments. Regarding the accuracy of the literature recommendation, the platform of this paper and the traditional method are 86.36% and 75.16%, respectively, and the recall rate is 67.29% and 57.25%, respectively. The platform scored 3.85 on the comprehensive evaluation of library intelligence service, close to the excellent level. Therefore, the platform can realize various functions, from resource collection and organization to resource processing and utilization, with certain practical significance.

Automatic Keyword Extraction Algorithm for Chinese Text based on Word Clustering

There are some problems in automatic keyword extraction of Chinese text, such as large feature extraction error, low precision of extracted keywords, and poor real-time performance. Therefore, an automatic keyword extraction algorithm for Chinese text based on word clustering is designed. Calculate keyword frequency, document frequency and inverse document frequency features through statistical algorithm, measure the degree of interdependence between keywords with the help of point mutual information, and construct keyword feature item quantification matrix with the help of vector space model corresponding to keywords and feature items to complete keyword feature quantification and realize keyword feature extraction of Chinese text. Calculate the average semantic similarity of keyword words, determine the similarity of keyword features, and eliminate the keyword features with high similarity; Set the comprehensive feature value of the importance of single word words in Chinese text, determine the importance of single word words in the text, remove the single word words with low importance, and use Bayesian framework to reduce the dimension of high-dimensional keyword feature data to realize preprocessing research. The mapping results of keyword vector space model are determined by word clustering algorithm, the text clusters of keyword space clustering results are calculated by clustering algorithm, and the keywords are classified by DBN method. On this basis, the automatic keyword extraction model of Chinese text is designed to realize the automatic keyword extraction of Chinese text. The experimental results show that the design algorithm can effectively reduce the feature extraction error and improve the extraction efficiency.

Finding Experts in Community Question Answering System Using Trie String Matching Algorithm with Domain Knowledge

Different community question-answering systems have been evolving as online fora for knowledge sharing among users across the world. However, finding experts on the questions is a key issue in the community question-answering system. To address this issue, a novel approach is proposed in this paper for finding experts from the community question-answering website using an exact string-matching algorithm along with domain knowledge. The proposed system is designed with three phases: i) User Profile Modeling, ii) Question Preprocessing, and iii) Expert Recommendation. Initially, the matrix factorization model is adopted for user profile modeling where the tags and the answerer’s information are represented in the form of a matrix. Then, the domain-wise grouping of tags is done to minimize the search time of the tags. The question preprocessing is done using a keyword extraction algorithm to extract the keywords. Finally, the expert recommendation is accomplished using the trie string matching algorithm and key-value mapping process. For doing experiments, a stack overflow community question-answering website is utilized in this work. The performance of the system is measured and the results section proved that the system achieved 91.2% accuracy.

Retracted: Research on Keyword Extraction Algorithm in English Text Based on Cluster Analysis.

[This retracts the article DOI: 10.1155/2022/4293102.].

Retracted: TextRank Keyword Extraction Algorithm Using Word Vector Clustering Based on Rough Data-Deduction.

[This retracts the article DOI: 10.1155/2022/5649994.].

Research on keyword extraction based on abstract extraction

In order to improve the accuracy of text keyword extraction, this paper combined with the relevant methods of abstract extraction, aiming to extract key sentences through the abstract extraction method, and then optimize the effect of keyword extraction. The experimental results show that the effect of the keyword extraction algorithm on the text after abstract extraction is improved by 6.92 percentage points.

A New Text-Mining–Bayesian Network Approach for Identifying Chemical Safety Risk Factors

The frequent occurrence of accidents in the chemical industry has caused serious economic loss and negative social impact. The chemical accident investigation report is of great value for analyzing the risk factors involved. However, traditional manual analysis is time-consuming and labor-intensive, while existing keyword extraction methods still need to be improved. This study aims to propose an improved text-mining method to analyze a large number of chemical accident reports. A workflow was designed for building and updating lexicons of word segmentation. An improved keyword extraction algorithm was proposed to extract the top 100 keywords from 330 incident reports. A total of 51 safety risk factors was obtained by standardizing these keywords. In all, 294 strong association rules were obtained by Apriori. Based on these rules, a Bayesian network was built to analyze safety risk factors. The mean accuracy and mean recall of the BM25 model in the comparison experiments were 10.5% and 14.38% higher than those of TF-IDF, respectively. The results of association-rule mining and Bayesian network analysis can clearly demonstrate the interrelationship between the safety risk factors. The methodology of this study can quickly and efficiently extract key information from incident reports which can provide managers with new insights and suggestions.

Research on Keyword Extraction Algorithm in English Text Based on Cluster Analysis.

How to facilitate users to quickly and accurately search for the text information they need is a current research hotspot. Text clustering can improve the efficiency of information search and is an effective text retrieval method. Keyword extraction and cluster center point selection are key issues in text clustering research. Common keyword extraction algorithms can be divided into three categories: semantic-based algorithms, machine learning-based algorithms, and statistical model-based algorithms. There are three common methods for selecting cluster centers: randomly selecting the initial cluster center point, manually specifying the cluster center point, and selecting the cluster center point according to the similarity between the points to be clustered. The randomly selected initial cluster center points may contain “outliers,” and the clustering results are locally optimal. Manually specifying the cluster center points will be very subjective because each person's understanding of the text set is different, and it is not suitable for the case of a large number of text sets. Selecting the cluster center points according to the similarity between the points to be clustered can make the selected cluster center points distributed in each class and be as close as possible to the class center points, but it takes a long time to calculate the cluster centers. Aiming at this problem, this paper proposes a keyword extraction algorithm based on cluster analysis. The results show that the algorithm does not rely on background knowledge bases, dictionaries, etc., and obtains statistical parameters and builds models through training. Experiments show that the keyword extraction algorithm has high accuracy and can quickly extract the subject content of an English translation.

Visual analytics and information extraction of geological content for text-based mineral exploration reports

A large amount of continuously increasing textual geoscience data is stored and not fully utilized. Text mining enables the discovery and analysis of valuable information,and presents valuable insights hidden in geological texts. This research aims to use text mining and visualization techniques to obtain content words -for the purpose of visually analyzing geological reports. The framework proposed in this study can enable researchers to quickly understand key information and improve the transmission efficiency of geological reports. First, we implemented an improved keyword extraction algorithm comprising the term frequency-inverse document frequency and word length to improve the accuracy of geological keyword extraction. Second, we extracted and visualized the relative importance as well as the links between content words that can represent the key information of geoscience reports using word-level information analysis and multidimensional scaling analysis. Finally, the keyword relevance and mutual clustering relations were visualized through graphs to provide an intuitive representation of the current state of the reports.

TextRank Keyword Extraction Algorithm Using Word Vector Clustering Based on Rough Data-Deduction.

When TextRank algorithm based on graph model constructs graph associative edges, the co-occurrence window rules only consider the relationships between local terms. Using the information in the document itself is limited. In order to solve the above problems, an improved TextRank keyword extraction algorithm based on rough data reasoning combined with word vector clustering, RDD-WRank, was proposed. Firstly, the algorithm uses rough data reasoning to mine the association between candidate keywords, expands the search scope, and makes the results more comprehensive. Then, based on Wikipedia online open knowledge base, word embedding technology is used to integrate Word2Vec into the improved algorithm, and the word vector of TextRank lexical graph nodes is clustered to adjust the voting importance of nodes in the cluster. Compared with the traditional TextRank algorithm and the Word2Vec algorithm combined with TextRank, the experimental results show that the improved algorithm has significantly improved the extraction accuracy, which proves that the idea of using rough data reasoning can effectively improve the performance of the algorithm to extract keywords.

Multifeature Fusion Keyword Extraction Algorithm Based on TextRank

Keyword extraction is the predecessor of many tasks, and its results directly affect search, recommendation, classification, and other tasks. In this study, we take Chinese text as the research object and propose a multi-feature fusion keyword extraction algorithm combined with BERT semantics and K-Truss graph(BSKT). The BSKT algorithm is based on the TextRank algorithm, which combines BERT semantic features, K-Truss features, and other features. First, the BSKT algorithm obtains the word vectors from the BERT pretraining model to calculate the semantic difference, which is used to optimize the iterative process of the TextRank word graph. Then, the BSKT algorithm obtains its K-Truss graph by decomposing the TextRank word graph and obtains the truss level feature of the word. Finally, by combining the word IDF and truss level features, the BSKT algorithm scores the words to extract keywords. Experimental results show that the BSKT algorithm achieves better performance than the latest keyword extraction algorithm SCTR in the task of extracting 1–10 keywords. Furthermore, the increment in F1 increased by 11.2% when the BSKT algorithm was used to extract three keywords from the Sensor dataset.

News keyword extraction algorithm based on semantic clustering and word graph model

The internet is an abundant source of news every day. Thus, efficient algorithms to extract keywords from the text are important to obtain information quickly. However, the precision and recall of mature keyword extraction algorithms need improvement. TextRank, which is derived from the PageRank algorithm, uses word graphs to spread the weight of words. The keyword weight propagation in TextRank focuses only on word frequency. To improve the performance of the algorithm, we propose Semantic Clustering TextRank (SCTR), a semantic clustering news keyword extraction algorithm based on TextRank. Firstly, the word vectors generated by the Bidirectional Encoder Representation from Transformers (BERT) model are used to perform k-means clustering to represent semantic clustering. Then, the clustering results are used to construct a TextRank weight transfer probability matrix. Finally, iterative calculation of word graphs and extraction of keywords are performed. The test target of this experiment is a Chinese news library. The results of the experiment conducted on this text set show that the SCTR algorithm has greater precision, recall, and F1 value than the traditional TextRank and Term Frequency-Inverse Document Frequency (TF-IDF) algorithms.

Keyword Extraction Algorithm for Classifying Smoking Status from Unstructured Bilingual Electronic Health Records Based on Natural Language Processing

Smoking is an important variable for clinical research, but there are few studies regarding automatic obtainment of smoking classification from unstructured bilingual electronic health records (EHR). We aim to develop an algorithm to classify smoking status based on unstructured EHRs using natural language processing (NLP). With acronym replacement and Python package Soynlp, we normalize 4711 bilingual clinical notes. Each EHR notes was classified into 4 categories: current smokers, past smokers, never smokers, and unknown. Subsequently, SPPMI (Shifted Positive Point Mutual Information) is used to vectorize words in the notes. By calculating cosine similarity between these word vectors, keywords denoting the same smoking status are identified. Compared to other keyword extraction methods (word co-occurrence-, PMI-, and NPMI-based methods), our proposed approach improves keyword extraction precision by as much as 20.0%. These extracted keywords are used in classifying 4 smoking statuses from our bilingual EHRs. Given an identical SVM classifier, the F1 score is improved by as much as 1.8% compared to those of the unigram and bigram Bag of Words. Our study shows the potential of SPPMI in classifying smoking status from bilingual, unstructured EHRs. Our current findings show how smoking information can be easily acquired for clinical practice and research.

Bug Severity Prediction using Keywords in Imbalanced Learning Environment

Reported bugs of software systems are classified into different severity levels before fixing them. The number of bug reports may not be equally distributed according to the severity levels of bugs. However, most of the severity prediction models developed in the literature assumed that the underlying data distribution is evenly distributed, which may not correct at all instances and hence, the aim of this study is to develop bug classification models from unevenly distributed datasets and tested them accordingly. To that end first, the topics or keywords of developer descriptions of bug reports are extracted using Rapid Keyword Extraction (RAKE) algorithm and then transferred them into numerical attributes, which combined with severity levels constructs datasets. These datasets are used to build classification models; Naïve Bayes, Logistic Regression, and Decision Tree Learner algorithms. The models’ prediction quality is measured using Area Under Recursive Operative Characteristics Curves (AUC) as the models learnt from more skewed environments. According to the results, the prediction quality of the Logistics Regression model is 0.65 AUC whereas the other two models recorded maximum 0.60 AUC. Though the datasets contain comparatively less number of instances from the high severity classes; Blocking and High, the Logistic Regression models predict the two classes with a decent AUC value of 0.65 AUC. Hence, this projects shows that the models can be trained from highly skewed datasets so that the models prediction quality is equally well over all the classes regardless of number of instances representing the class. Further, this project emphasizes that the models should be evaluated using the appropriate metrics when the models are trained from imbalance learning environments. Also, this work uncovers that the Logistic Regression model is also capable of classifying documents as Naïve Bayes, which is well known for this task.

Keyword extraction method for machine reading comprehension based on natural language processing

The traditional keyword extraction method has the problem that the accuracy of identification and extraction decreases when the amount of data is increasing. This paper introduces natural language processing technology and designs a keyword extraction method for machine reading comprehension. In this process, considering that the information in the text of machine reading comprehension may come from the web page, we can refer to the NLP system of natural language algorithm. Firstly, analyze the content of a single document, then analyze the short string level, and finally organize the preprocessing of natural language text information, so as to achieve the preprocessing of machine reading and comprehension of Chinese text. At the same time, the SKEA keyword extraction algorithm system is designed to merge text words with the same meaning, locate the location information of document resources, and identify the annotated words in the resource base. After the annotation processing is completed, the annotation information is extracted. In addition, a comparative experiment is presented to prove the effectiveness of the proposed method compared with the traditional method.