Keyword Extraction Research Articles

Research on Topic Discovery and Evolution Trend Based on Temporal Keyword Characteristics Analysis

In order to mine the research topics in massive articles, sort out the evolution context and correlation relationship of research topics, enhance the scientificity and vividness of evolution results, this paper proposes the concept of temporal influence factor as an important feature in keyword extraction. Using the time window method, the topic model is used to mine and identify topics and perform visual analysis. It is verified that keyword extraction with time series features can improve the effect of topic model. Through visualization, we can not only observe the overall trend of topic popularity, but also analyze the evolution of topic content in each time period and observe the trend of splitting and merging.

Automatic extraction of significant terms from the title and abstract of scientific papers using the machine learning algorithm: A multiple module approach

Keyword extraction is the task of identifying important terms or phrase that are most representative of the sourcedocument. Although the process of automatic extraction of keywords from title is an old method, it was mainly forextraction from a single web document. Our approach differs from previous research works on keyword extraction in severalaspects. For those who are non-expert of the scientific fields, understating scientific research trends is difficult. The purposeof this study is to develop an automatic method of obtaining overviews of a scientific field for non-experts by capturingresearch trends. This empirical study excavates significant term extraction using Natural Language Processing (NLP) tools.More than 15000 titles saved in a .csv file was our dataset and scripts written in Python were our process to compare how farsignificant terms of scientific title corpus are similar or different to the terms available in the abstract of that same scientificarticle corpus. A light-weight unsupervised title extractor, Yet Another Keyword Extractor (YAKE) was used to extract theresults. Based on our analysis, it can be concluded that these algorithms can be used for other fields too by the non-expertsof that subject field to perform automatic extraction of significant words and understanding trends. Our algorithm could be asolution to reduce the labour-intensive manual indexing process.

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.

ПРИМЕНЕНИЕ АЛГОРИТМОВ ФОРМИРОВАНИЯ ВОПРОСОВ ПО ТЕКСТУ ДЛЯ АВТОМАТИЧЕСКОЙ ГЕНЕРАЦИИ ТЕСТОВ

The article presents findings of manual, semi-automatic, and automatic approaches to gen-erate test questions based on such methods as annotation, keyword extraction, and learning datasets for com-piling tests for studying material, along with a description of each method algorithm, examples of generated questions, and their quality assessment. These examples demonstrate the advantages of an algorithm for gen-erating a method using a dataset and a combination of methods, as well as their possible practical application.

Keyword extraction from news corpus by deep learning in the context of internet of things

Keyword extraction from news corpus by deep learning in the context of internet of things

An Efficient Long Short-Term Memory Model for Digital Cross-Language Summarization

The rise of social networking enables the development of multilingual Internet-accessible digital documents in several languages. The digital document needs to be evaluated physically through the Cross-Language Text Summarization (CLTS) involved in the disparate and generation of the source documents. Cross-language document processing is involved in the generation of documents from disparate language sources toward targeted documents. The digital documents need to be processed with the contextual semantic data with the decoding scheme. This paper presented a multilingual cross-language processing of the documents with the abstractive and summarising of the documents. The proposed model is represented as the Hidden Markov Model LSTM Reinforcement Learning (HMMlstmRL). First, the developed model uses the Hidden Markov model for the computation of keywords in the cross-language words for the clustering. In the second stage, bi-directional long-short-term memory networks are used for key word extraction in the cross-language process. Finally, the proposed HMMlstmRL uses the voting concept in reinforcement learning for the identification and extraction of the keywords. The performance of the proposed HMMlstmRL is 2% better than that of the conventional bi-direction LSTM model.

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

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

Exploring the Potential of Machine Learning Techniques for Predicting Travel Insurance Claims: A Comparative Analysis of Four Models

Travel insurance is a crucial component for any traveller as it offers protection against financial losses resulting from unforeseen events during a trip, such as trip cancellations, medical emergencies, lost luggage, and related issues. This study aims to investigate the potential of machine learning (ME) techniques for predicting the probability of travel insurance claims. In order to tackle the issue of managing extensive and intricate datasets, advanced statistical techniques were employed, including keyword extraction, feature extraction, and Chi-squared tests. Our evaluation of four popular ML models, namely balanced random forest (BRF), support vector machines (SVM), logistic regression (LR), and balanced bagging (BB), highlight that the BRF model outperforms the other models in predicting travel insurance claims. Our study emphasises the advantages of utilising machine learning algorithms in processing large datasets, producing predictions on future insurance claims, and adapting to changing circumstances, thus serving as a valuable tool for practitioners in the travel insurance industry.

Adaptive Emulation Framework for Multi-Architecture IoT Firmware Testing

Internet of things (IoT) devices are being increasingly used in numerous areas. However, the low priority on security and various IoT types have made these devices vulnerable to attacks. To prevent this, recent studies have analyzed firmware in an emulation environment that does not require actual devices and is efficient for repeated experiments. However, these studies focused only on major firmware architectures and rarely considered exotic firmware. In addition, because of the diversity of firmware, the emulation success rate is not high in terms of large-scale analyses. In this study, we propose the adaptive emulation framework for multi-architecture (AEMA). In the field of automated emulation frameworks for IoT firmware testing, AEMA considers the following issues: (1) limited compatibility for exotic firmware architectures, (2) emulation instability when configuring an automated environment, and (3) shallow testing range resulting from structured inputs. To tackle these problems, AEMA can emulate not only major firmware architectures but also exotic firmware architectures not previously considered, such as Xtensa, ColdFire, and reduced instruction set computer (RISC) version five, by implementing a minority emulator. Moreover, we applied the emulation arbitration technique and input keyword extraction technique for emulation stability and efficient test case generation. We compared AEMA with other existing frameworks in terms of emulation success rates and fuzz testing. As a result, AEMA succeeded in emulating 864 out of 1,083 overall experimental firmware and detected vulnerabilities at least twice as fast as the experimental group. Furthermore, AEMA found a 0-day vulnerability in real-world IoT devices within 24 h.

Smart Resume Analyser: A Case Study using RNN-based Keyword Extraction

The pursuit of a reputable position in a company is a common goal shared by many people. For making this desire into reality their resume is the key to it. Making these resumes is a challenging task for many and modifying it is another time-consuming and burdensome task. Taking these problems into consideration and to make eye-catching resumes we have developed a smart resume analyser that can analyse the user’s resume, it intelligently identifies their skills and qualifications, enabling us to suggest the best-suited job titles for their profile. Furthermore, based on this analysis, our system generates recommendations for optimizing and enhancing the user’s resume, making it more appealing to potential employers. Through the utilization of NLP and ML technologies, our resume analyser provides personalised and effective solutions to job seekers, helping them stand out in today’s competitive job market. The information on the resume can be analysed and understood easily as NLP has the capability to understand and parse the information on the resume and extract the desired information efficiently. With the additional help from Python and its packages like streamlit, pymysql, etc. we can store the extracted data and give a rating based on the analysis of the stored data. After the rating is generated, we recommend some modifications like: adding sentences like my hobbies are, my goal is, add objectives, and add declaration and more. We also suggest a few sources through which the user can enhance their skills.

Design of social media information extraction system based on deep learning

Aiming at the problems of low accuracy and long time in traditional systems, a social media information extraction system based on deep learning is designed. Firstly, the overall framework of the system is designed, including text extraction module, keyword extraction module and emotion analysis module. Then, the social media information is preprocessed, the emotional resource establishment and information extraction rules are constructed according to the preprocessing results, and the convolution neural network is used to construct the social media information extraction model. Finally, according to the correlation between text entries and categories, the global MI values of entries and all categories are calculated. The calculation results are inputted into the constructed convolution neural network model, and the social media information extraction results are output. The simulation results show that the extraction accuracy of the designed system is high and the extraction time is within 15 s.

Comparative analysis of TF-IDF and loglikelihood method for keywords extraction of twitter data

Twitter has become the foremost standard of social media in today’s world. Over 335 million users are online monthly, and near about 80% are accessing it through their mobiles. Further, Twitter is now supporting 35+ which enhance its usage too much. It facilitates people having different languages. Near about 21% of the total users are from US and 79% of total users are outside of US. A tweet is restricted to a hundred and forty characters; hence it contains such information which is more concise and much valuable. Due to its usage, it is estimated that five hundred million tweets are sent per day by different categories of people including teacher, students, celebrities, officers, musician, etc. So, there is a huge amount of data that is increasing on a daily basis that need to be categorized. The important key feature is to find the keywords in the huge data that is helpful for identifying a twitter for classification. For this purpose, Term Frequency-Inverse Document Frequency (TF-IDF) and Loglikelihood methods are chosen for keywords extracted from the music field and perform a comparative analysis on both results. In the end, relevance is performed from 5 users so that finally we can take a decision to make assumption on the basis of experiments that which method is best. This analysis is much valuable because it gives a more accurate estimation which method’s results are more reliable.

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

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

Open Domain Response Generation Guided by Retrieved Conversations

Open domain response generation is the task of creating a response given a user query in any topics/domain. Limited by context and reference information, responses generated by current systems are often “bland” or generic. In this paper, we combine a response generation model with a retrieval system that searches for relevant utterances and responses. The generation model has two main components: a keyword extraction module and a two-stage transformer. The keyword extraction module aims to extract two types of keywords in an unsupervised fashion from the retrieved results: (1) keywords in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">query</i> not found in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">retrieved utterances</i> (DiffKey), and (2) overlapping keywords among the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">retrieved responses</i> (SimKey). Given these keywords, the two-stage transformer first decides where to insert the keywords in the response, and the second generates the full response given the location of the keywords. The keyword extraction module and the two-stage transformer are connected in a single network, and so our system is trained end-to-end. Experimental results on Cornell Movie-Dialog corpus, Douban and Weibo demonstrate that our model outperforms state-of-the-art systems in terms of ROUGE, relevance scores and human evaluation.

SDOT: Secure Hash, Semantic Keyword Extraction, and Dynamic Operator Pattern-Based Three-Tier Forensic Classification Framework

Most traditional digital forensic techniques identify irrelevant files in a corpus using keyword search, frequent hashes, frequent paths, and frequent size methods. These methods are based on Message Digest and Secure Hash Algorithm-1, which result in a hash collision. The threshold criteria of files based on frequent sizes will lead to imprecise threshold values that result in an increased evaluation of irrelevant files. The blacklisted keywords used in forensic search are based on literal and non-lexical, thus resulting in increased false-positive search results and failure to disambiguate unstructured text. Due to this, many extraneous files are also being considered for further investigations, exacerbating the time lag. Moreover, the non-availability of standardized forensic labeled data results in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(O(2^{n}))$ </tex-math></inline-formula> time complexity during the file classification process. This research proposes a three-tier Keyword Metadata Pattern framework to overcome these significant concerns. Initially, Secure Hash algorithm-256 hash for the entire corpus is constructed along with custom regex and stop-words module to overcome hash collision, imprecise threshold values, and eliminate recurrent files. Then blacklisted keywords are constructed by identifying vectorized words that have proximity to overcome traditional keyword search’s drawbacks and to overcome false positive results. Dynamic forensic relevant patterns based on massive password datasets are designed to search for unique, relevant patterns to identify the significant files and overcome the time lag. Based on tier-2 results, files are preliminarily classified automatically in O(log n) complexity, and the system is trained with a machine learning model. Finally, when experimentally evaluated, the overall proposed system was found to be very effective, outperforming the existing two-tier model in terms of finding relevant files by automated labeling and classification in O(nlog n) complexity. Our proposed model could eliminate 223K irrelevant files and reduce the corpus by 4.1% in tier-1, identify 16.06% of sensitive files in tier-2, and classify files with 91% precision, 95% sensitivity, 91% accuracy, and 0.11% Hamming loss compared to the two-tier system.

A Comparative Assessment of Unsupervised Keyword Extraction Tools

A Comparative Assessment of Unsupervised Keyword Extraction Tools

Institutional Repository Keyword Analysis with Web Crawler

This study aims at investigating procedures of semantic and linguistic extraction of keywords from metadata of documents indexed in the Institutional Repository Unesp. For that purpose, a web crawler was developed, that collected 325.181 keywords from authors, in all fields of knowledge, from February 28th, 2013 to November 10th, 2021. The preparation of the collection, extraction and analysis environment used the Python programming language, composed of three program libraries: library requests, which allows manipulation of hyperlinks of webpages visited through web crawler; BeautifulSoup library, used to extract HTML data through webpage analysis; and Pandas library, which has an open code (free software) and stands for providing tools for high performance data manipulation and analysis. The final listing consisted of 273,485 keywords, which represents 15.9% of the listing initially collected. Results indicated that the most recurring problem was the duplication of keywords, with 51,696 duplicated keywords, representing indicators of inconsistencies in the search for documents. It is concluded that the refinement of keywords assigned by authors eliminates the incorporation of a set of symbols that do not represent the authors’ keywords with the same spelling, but with upper/lower case variations or lexical variations indexing different documents.

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.

Keyword Extraction: A Modern Perspective

The goal of keyword extraction is to extract from a text, words, or phrases indicative of what it is talking about. In this work, we look at keyword extraction from a number of different perspectives: Statistics, Automatic Term Indexing, Information Retrieval (IR), Natural Language Processing (NLP), and the emerging Neural paradigm. The 1990s have seen some early attempts to tackle the issue primarily based on text statistics [13, 17]. Meanwhile, in IR, efforts were largely led by DARPA’s Topic Detection and Tracking (TDT) project [2]. In this contribution, we discuss how past innovations paved a way for more recent developments, such as LDA, PageRank, and Neural Networks. We walk through the history of keyword extraction over the last 50 years, noting differences and similarities among methods that emerged during the time. We conduct a large meta-analysis of the past literature using datasets from news media, science, and medicine to business and bureaucracy, to draw a general picture of what a successful approach would look like.

Data Mining in Coal-Mine Gas Explosion Accidents Based on Evidence-Based Safety: A Case Study in China

From an informatics perspective, decision-making failures in accident prevention are due to insufficient necessary safety evidence. Analyzing accident data can help in obtaining safety evidence. Currently, such a practice mostly relies on experts’ judgement and experience, which are subjective and inefficient. Furthermore, due to the inadequate safety-related theoretical support, the sustainable safety of a system can hardly be achieved purposefully. To automatically explore and obtain latent safety evidence in coal-mine data, and improve the reliability and sustainability of coal-mine safety management, a novel framework of combining data mining technology and evidence-based safety (EBS) theory is proposed, and was applied to a coal gas explosion accident. First, the term frequency-inverse document (TF-IDF) and TextRank algorithms were fused to extract keywords, and keyword evolution word cloud maps from the time dimension were drawn to obtain keyword safety evidence. Then, on the basis of the latent Dirichlet allocation (LDA) model, the best safety evidence, such as accident causation topics and causation factors, were mined, and safety decisions were given. The results show that accident data mining, based on evidence-based safety, can effectively and purposefully mine the best safety evidence, and guide safety decision making to optimize safety management models and achieve sustainable safety.