Text Ranking Research Articles

Neural Network Language Models, Mutual Sense Affinity, and Short Text Ranking According to Closeness to a Semantic Standard

Neural Network Language Models, Mutual Sense Affinity, and Short Text Ranking According to Closeness to a Semantic Standard

Optimized automated testing: test case generation and maintenance using latent semantic analysis-based TextRank and particle swarm optimization algorithms

Software development would have to include automated testing to ensure the finished product and performs as intended. However, the process of Test Case Generation and Maintenance can be time-consuming and error-prone, especially when manual methods are used. This research proposes a new approach to improve the efficiency and accuracy of automated testing using latent semantic analysis (LSA)-based TextRank (TR) and particle swarm optimization (PSO) algorithms. The study aims to evaluate the effectiveness of these algorithms in generating and optimizing test cases based on requirements analysis. To retrieve key information from the criteria, methods including text classification (TC), named entity recognition (NER), and sentiment analysis (SA) are used to evaluate the text. Test cases are then generated using LSA-based TR for text summarization and PSO for optimization. The aim of this work is to identify any limitations that need to be addressed and to evaluate the overall efficiency and accuracy of automated testing (AT) using proposed algorithms. The results of this research are expected to have important implications for the software industry, helps to improve the overall efficiency and accuracy of AT. The findings could guide future research that led to the creation of more advanced and effective tools for AT.

EASESUM: an online abstractive and extractive text summarizer using deep learning technique

<div align="center"><table width="590" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p class="CM12">Large volumes of information are generated daily, making it challenging to manage such information. This is due to redundancy and the type of data available, most of which needs to be more structured and increases the amount of search time. Text summarization systems are considered a real solution to this vast amount of data because they are used for document compression and reduction. Text summarization keeps the relevant information and eliminates the text's non-relevant parts. This study uses two types of summarizers: Extractive Text summarizers and Abstractive text summarizers. The Text Rank Algorithm was used to implement the Extractive summarizer, while Bi-directional Recurrent Neural Network (RNN) was used to implement the Abstractive text summarizer. To improve the quality of summaries produced, word embedding was also used. For the evaluation of the summarizers, the ROUGE evaluation system was used. ROUGE contrasts summaries created by hand versus those created automatically. ROUGE examination of the produced summary revealed the superiority of human-produced summaries over those generated automatically. For this paper, a summarizer was implemented as a Web Application. The average ROUGE recall score ranging from 30.00 to 60.00 for abstractive summarizer and 0.75 to 0.82 for extractive text showed an encouraging result.</p></td></tr></tbody></table></div>

On Embedding Implementations in Text Ranking and Classification Employing Graphs

This paper aims to enhance the Graphical Set-based model (GSB) for ranking and classification tasks by incorporating node and word embeddings. The model integrates a textual graph representation with a set-based model for information retrieval. Initially, each document in a collection is transformed into a graph representation. The proposed enhancement involves augmenting the edges of these graphs with embeddings, which can be pretrained or generated using Word2Vec and GloVe models. Additionally, an alternative aspect of our proposed model consists of the Node2Vec embedding technique, which is applied to a graph created at the collection level through the extension of the set-based model, providing edges based on the graph’s structural information. Core decomposition is utilized as a method for pruning the graph. As a byproduct of our information retrieval model, we explore text classification techniques based on our approach. Node2Vec embeddings are generated by our graphs and are applied in order to represent the different documents in our collections that have undergone various preprocessing methods. We compare the graph-based embeddings with the Doc2Vec and Word2Vec representations to elaborate on whether our approach can be implemented on topic classification problems. For that reason, we then train popular classifiers on the document embeddings obtained from each model.

Functional Analysis of English Carriers and Related Resources of Cultural Communication in Internet Media

Abstract In the Internet intelligent teaching platform, students’ demand for English cultural content is increasingly obvious. To help students quickly locate the overall content of resources in online autonomous learning, this study constructs a video annotation model for online teaching. This method classifies text by designing an optimized Bidirectional Encoder Representation from the Transformers model and designs a Text Rank keyword extraction model that integrates external knowledge and semantic feature weights. The extraction of knowledge points contained in audio and video resources can be realized. In the experimental data set, a relatively complete video content summary could be obtained by combining the first three sentences with the last two sentences. The F1 value of the classification model was up to 91.3%. In addition, the BERT-T model proposed in this article had the best effect on the experiment. Compared with the original BERT model, the macro-F1 was 0.8% higher and 0.5% higher than the Ro BERTA model. In the keyword extraction experiment, B-Text Rank was 2.19 and 2.85% higher than the traditional Text Rank in the two datasets. The experiment shows that the BERT-Text Rank network resource annotation model has excellent application performance in English online autonomous teaching and could guide students to learn.

Text Intelligence Engine: ‘Unleashing the power of Summarization with Machine Learning.’

In the era of exponential growth in online data, the proliferation of online information necessitates effective strategies for text summarization to distill vast amounts of data into concise, digestible insights. This paper presents a comprehensive investigation into text summarization methodologies within the context of a Text Intelligence Engine project, with a primary emphasis on the Text Rank algorithm. Leveraging the principles of graph-based ranking, our approach harnesses the power of Text Rank to identify key concepts and generate coherent summaries. Through meticulous experimentation and rigorous evaluation, we demonstrate the robustness and effectiveness of Text Rank in extracting salient information from diverse textual sources. Furthermore, we explore the integration of Text Rank with machine learning techniques to enhance summarization performance. By elucidating the theoretical foundations and practical applications of Text Rank, this paper contributes to the advancement of text intelligence systems, offering valuable insights and guidelines for researchers and practitioners in the field. Keywords -Natural Language Processing, NLKT, Text Rank Algorithm, Supervised Learning, Random Algorithm, Text summarization, Extractive summarization, Abstractive summarization, Machine learning, SpaCy, Neural networks, Rhetorical structure theory

Knowledge fusion method of power grid model based on Seq2seq half pointer and half label method

Due to the complexity of the calculation process of the existing methods, the efficiency of data fusion of the power grid model is low. In order to improve the knowledge fusion effect of power grid model, this paper studied the knowledge fusion method of power grid model based on Seq2seq half pointer and half label method. The Text Rank algorithm is used to calculate the weight of semantic nodes of each grid model, and combined with the topological potential method, the semantic information of the grid model is extracted according to the final weight value, and the Seq2Seq semi-pointer semi-label model framework is constructed. The data of the scheduling automation system OMS and the production management system PMS are used as input. The extracted candidate mesh model semantics and the original mesh model semantics are encoded by Seq2Seq half-pointer half-label model. The semantic data of the power grid model is fused and sent to the Seq2Seq encoder. After the training is completed, the effective information is extracted from the power grid model through the Seq2Seq model to complete the knowledge fusion of the power grid model. Experimental results show that this method eliminates the redundant part of the basic attributes of each data source in the substation grid model after knowledge fusion, and the description of each basic attribute is more standardized, unified and perfect. Under different mesh model data dimensions, the support of the proposed method is all above 98%. The model trained by the proposed method tends to be stable after 120 iterations, and the precision, recall and F1 of the test set are 0.98, 0.93 and 0.91, respectively. At the same time, this method has high efficiency in the knowledge fusion processing of the power grid model, and its data processing speed is less than 160 s. The average integrity of the private data of the power grid model is 98.86%, indicating that the proposed method can better ensure the integrity of the data. Finally, compared with the application of other methods under different data amounts, the mean square error obtained by the proposed method is the smallest, indicating that the proposed method effectively improves the fusion accuracy.

Exploring the Knowledge Structure and Trends for Severe COVID-19 Risk Factors Using Text Network Analysis.

This study was aimed to identify knowledge structure and trends in severe COVID-19 risk factor using text network analysis. The 22,628 papers published during from January 2020 to December 2021. We analyzed and visualized using Text Rank analyzer and Gephi software. They were grouped into 5 central themes - biomedical factors, occupational environmental factors, demographic factors, health behavior factors, and complications. The emerging topics were identified to the chronological trends. This study can promote a systematic understanding of severe COVID-19 risk factors.

Summarization of Software Bug Report based on Sentence Semantic Similarity (SSBRSSS) Technique

In this work, sentence similarity between sentences of software bug report is computed. For this, two methods are utilized, Latent Semantic Analysis and Text Rank. Latent Semantic Analysis is used to compute semantic similarity between sentences of bug reports which infers deeper and hidden relation between words. From this, a pair of sentences with semantic similarity above a set threshold is selected and from one pair of sentences, only one sentence is selected. The remaining sentences are passed into TextRank algorithm and sentences with high similarity are further selected to generate a coherent summary. The proposed approach is evaluated on a newly constructed Apache Project Bug Report Corpus and existing Bug Report Corpus. The proposed approach is also compared with baseline approaches that mainly focus on only lexical similarity. The results when evaluated on Apache project Bug Report Corpus attains an average value of 80%, 72.57%, 76.05% and 76.57% in terms of precision, recall, F-score and pyramid precision respectively.

Exploring the Path of Judicial Big Data to Enhance Data Governance Capability

Abstract In ample data justice, predicting legal outcomes and identifying similar cases hold significant value. This paper presents an advanced legal prediction algorithm that integrates the specific features of legal texts. Utilizing the Text Rank model, it extracts essential text features from legal provisions and facts, enabling the precise deployment of legal requirements based on detailed case analyses and legal knowledge. To overcome the hurdles of scant training data and the challenge of distinguishing similar legal documents, we developed a similar case matching model employing twin Bert encoders. Our empirical study reveals theft, intentional injury, and fraud as the predominant crimes, with sample counts of 335,745, 174,526, and 47,677, respectively. These top offenses, correlating with the most frequently cited laws, account for 85.79% of our dataset. The analysis further indicates “RMB” as the most recurring word in theft and fraud cases, and “minor injury” in intentional injury instances. Notably, our findings show that categories such as “misappropriation” are prone to misclassification as “embezzlement,” and “robbery” often gets confused with “theft,” highlighting the complexities of legal classification.

Research on the quantitative model of the communication effect of Civic and Political Education for social platform data

Abstract Social platforms have revolutionized the lives of the public, provided a resourceful educational platform for the ideological and political education of college students, and become the distribution center of ideological and political education information and the amplifier of social opinion. In this paper, we concentrate on the dissemination of data related to ideological and political education on social platforms. We start by segmenting the text and then use the IF-IDF algorithm, the Text Rank algorithm, and the LDA theme model to process various text features. Simultaneously, we will employ a sentiment dictionary and simple Bayesian analysis, along with a convolutional neural network, to measure the impact of emotion propagation. The microblogging user’s comment data for the Civics communication emotion analysis concluded that the user group of the initial period of the Civics education activities will produce some negative emotions, while at the same time, the positive emotion has been maintaining an upward trend. While the negative emotions began to decline and slow down, the user group with the highest positive emotion reached a value of 6.35464.

Analysis and Research on Design of Dialect Emoticons from the Perspective of Regional Culture in Yancheng

Abstract In this paper, Yancheng’s local culture is classified through literature research, expert interviews, and popular questionnaires. In the dialect matching of emoticons, the high-frequency text used by Yancheng people is used, and the dialect text ranking table measured by experts is combined to provide the main content for producing and designing emoticons. Based on the analysis model of Yancheng material culture and nonmaterial culture, the image design elements of Yancheng regional emojis with local and high cultural characteristics are extracted from Yancheng regional elements. The Design of emoticons that combine pictures and text emphasizes the expression of awareness, starting with modeling the theme image, color matching, scene props, and so on. The animation emoji industry benefits from this valuable feasibility strategy, and new vitality is added to the development of the animation industry in Yancheng.

Integration of Transformer Model Text Summarization and Text-to-Speech in Helping Document Understanding in the Bukudio Application

The need for effective, accurate and precise understanding of information will provide optimization of the decision-making process, increase knowledge and quality of life. Understanding information in relation to the document summarization process, if done manually, sometimes takes quite a long time. Text summarization techniques which are useful as document summarizers have been developed and applied to various things such as summarizing important documents, news texts or customer feedback. In this article, text summarization using the text rank method and transformer modeling integrated with text to speech techniques is developed in the Bukudio application, which is an application that provides audio versions of book documents in the application database. Based on the test results, the evaluation process was carried out using the Rouge method and gave the best results in calculating the Rouge 1 overlap monogram resulting in 0.523 for the F1 Score value, 0.434 for the precision value and 0.659 for the recall value. This research will be developed using other methods so that not only files in PDF document format can be processed, but other EPUB (Electronic Publication) files.

Evaluation of text summarization techniques in healthcare domain: Pharmaceutical drug feedback

Text summarization techniques offer a way to address the significant challenges faced by clinicians and researchers due to the exponential growth of information in healthcare on the internet. By condensing lengthy text into concise summaries, these techniques facilitate faster, easier, and convenient access to relevant information. This is particularly beneficial in use cases such as online user feedback/reviews about drugs, where valuable insights can be obtained that extend beyond clinical trials and observational studies. This paper comprehensively evaluates six widely used text summarization techniques (LSA, Luhn’s Method, Text Rank, T5 Transformer, and Kullback-Leibler, BERT) in extracting key insights, themes and patterns about drugs from online drug reviews. The evaluation considers both quantitative and qualitative aspects, focusing on their applicability to the challenging medical terminology, which is known for its inherent intricacies and complexities. The findings of this study showed the performance of text summarization techniques using metrics such as F1 score, Recall, and Precision, focused on the unigram, bigram, and trigram overlap between the generated text summaries and the reference summaries, utilizing the ROUGE-1, ROUGE-2, and ROUGE-L evaluation methods. It is shown that results showed TextRank to be the most effective text summarization method followed by BERT when working with Medical Terminology in Healthcare & Biomedical Informatics, given its complex hierarchy and extensive vocabulary of medical terms.

Deep Semantic Understanding and Sequence Relevance Learning for Question Routing in Community Question Answering

Question routing (QR) aims to route newly submitted questions to the potential experts most likely to provide answers. Many previous works formalize the question routing task as a text matching and ranking problem between questions and user profiles, focusing on text representation and semantic similarity computation. However, these works often fail to extract matching features efficiently and lack deep contextual textual understanding. Moreover, we argue that in addition to the semantic similarity between terms, the interactive relationship between question sequences and user profile sequences also plays an important role in matching. In this paper, we proposed two BERT-based models called QR-BERTrep and QR-tBERTint to address these issues from different perspectives. QR-BERTrep is a representation-based feature ensemble model in which we integrated a weighted sum of BERT layer outputs as an extra feature into a Siamese deep matching network, aiming to address the non-context-aware word embedding and limited semantic understanding. QR-tBERTint is an interaction-based model that explores the interactive relationships between sequences as well as the semantic similarity of terms through a topic-enhanced BERT model. Specifically, it fuses a short-text-friendly topic model to capture corpus-level semantic information. Experimental results on real-world data demonstrate that QR-BERTrep significantly outperforms other traditional representation-based models. Meanwhile, QR-tBERTint exceeds QR-BERTrep and QR-BERTint with a maximum increase of 17.26% and 11.52% in MAP, respectively, showing that combining global topic information and exploring interactive relationships between sequences is quite effective for question routing tasks.

Retracted: Construction of Machine Learning Model Based on Text Mining and Ranking of Meituan Merchants

Retracted: Construction of Machine Learning Model Based on Text Mining and Ranking of Meituan Merchants

Text Summarization Using Natural Language Processing

The availability of information today accessible in digital form has accelerated. Retrieving useful document from such large pool of information gets difficult. So, to summarize these text documents is very crucial. Text summarization is a process of minimizing the original source document to get essential information of that document. It eliminates the redundant, less important content and provides you with the vital information in a shorter version usually half a length of the original text. Creating a manual summary is a very time-consuming task. Automatic summarization helps in getting the gist of information present in a particular document in a very short period. In the comparison of all Indian regional languages, there is very less amount of work done for summarization of Hindi documents. This paper presents an effective way to summarize using a Text Rank algorithm. It focuses on summarizing single Hindi text document at a time based on natural language processing (NLP).

Big data-assisted urban governance: An intelligent real-time monitoring and early warning system for public opinion in government hotline

The implementation of big data-based analysis, conducting forecasts, and increasing service capabilities in smart government affairs can effectively enhance the efficiency of urban emergency management. The government hotline is an effective citizen relationship management (CRM) tool that generates a large amount of appeal information every day. Identifying and predicting public opinion hotspots of citizen complaints in real time and accurately identifying periodic and mass incidents present major challenges to city managers. In this study, we propose a pattern-based identification and early warning system for public opinion. This system uses an improved mining algorithm for frequent patterns to accurately identify topics and their corresponding case information and adopts a word-weight method to assign weights to frequent patterns, such that more important information would have a higher weight. Cosine similarity is used to calculate a similarity matrix for frequent patterns in the text content, thereby accurately distinguishing between repeated events and events of major interest. Moreover, a hash table-based retrieval and improved text rank are proposed to extract text summaries. Finally, we define sudden issues of major interest and develop an identification and early warning system for public opinion that is easy to operate, with effective user interaction and data visualization interfaces. A real case study is implemented to experimentally identify public opinion accurately and perform early. The average accuracy rate of data mining reached 87.95% in the first half of the operational evaluation of the system. Besides, when compared with the analysis of the conventional SQL statements, the retrieval efficiency is improved by 6 times and supports multi-keyword retrieval. Consequently, the enhanced text rank summary extraction algorithm improves p@10 accuracy by 6%.

Pretrained Transformers for Text Ranking: BERT and Beyond

Pretrained Transformers for Text Ranking: BERT and Beyond

ТЕМАТИЧЕСКОЕ МОДЕЛИРОВАНИЕ И СУММАРИЗАЦИЯ ТЕКСТОВ В ОБЛАСТИ КИБЕРБЕЗОПАСНОСТИ

Purpose: improving the quality of text document analysis through the use of machine learning and intellectual analysis models in summarizing and topic modeling tasks, which will reduce the load on an expert who analyzes and generalizes significant volumes of semi-structured text data on information security topics from various sources. Methods: machine learning methods were used for the operational processing and analysis of large volumes of heterogeneous ill-structured information in natural language (NL). Methods of thematic modeling and summarization of texts based on deep neural networks, including neural network language models based on the architecture of transformers, are applied. Practical relevance: the main stages of the machine procedure of thematic modeling and summarization of professional texts in the field of information security are highlighted. The results of a comparative evaluation of the effectiveness of using clustering models, latent semantic analysis, Fast Text, Text Rank language models and BERT transformers for these purposes are presented. Recommendations are given regarding the prospects for the practical application of these models as a means of intellectual support for the professional activities of cybersecurity specialists. Scientific novelty: a complex of machine learning models for thematic modeling and summarization of professional texts is proposed, based on neural network models of attachments and transformer models, characterized by an algorithm for preparing a corpus of texts for training models and the use of a learning transfer algorithm, which will increase the efficiency of analysis and generalization of domain-specific corpora of texts.