Keyword Extraction Techniques Research Articles

A novel fuzzy logic-based approach for textual documents indexing

In the evolving landscape of information retrieval and natural language processing, the quest for more effective automatic keyword extraction (AKE) techniques from textual documents has become a pivotal research focus. Existing methodologies, while offering valuable insights, often grapple with the challenges posed by the imprecision and variability inherent in human language. This has led to a growing recognition of the need for innovative approaches to navigating textual content’s nuances more adeptly. In response to this imperative, this paper proposes a novel fuzzy indexing approach designed specifically for the indexing of textual documents. Fuzzy indexing, grounded in the principles of fuzzy logic, provides solutions for handling the inherent uncertainty and imprecision in natural language, especially when confronted with the intricacies of linguistic ambiguity and variability. By leveraging the power of fuzzy logic, we aim to enhance the precision of keyword extraction. This paper unfolds the intricacies of our fuzzy indexing approach, detailing the theoretical methodology through empirical evaluation and comparative analysis; we seek to demonstrate the efficacy of our approach in outperforming traditional methods in the context of fuzzy indexing for textual documents.

Creating a Visual Topic Map for Battery Researchers Using a Large Global Open Dataset

Battery related research has gained increasing attention in recent years. A large number of research papers about battery are being published as open access. In order to explore the research content in these papers efficiently, it is necessary to develop information management tools to capture the knowledge embedded in them.We are working on a project to create a visual topic map for battery researchers. This map connects battery researchers according to their research topics. This allows potential connection between researches with similar research topics, which increase collaboration opportunities to advance battery research. Additionally, visualization of research content supports intuitive capturing of research trend by maximizing information absorbance.We extract all published works related to battery research from OpenAlex1, which is a fully open catalog of the global research that offers significant advantages in terms of data coverage and affordability and openness. Anther important feature of OpenAlex is that, every article is tagged with multiple concepts using automatic classifier. In addition to concepts extracted from OpenAlex dataset, we used a pre-trained large language model, named KeyBERT2, to further extract representative terms from the work abstract. KeyBERT is a minimal and easy-to-use keyword extraction technique based on the transformers model.For each researcher, we have extracted all concepts and terms from all the articles that authored by that author. Using those terms and concepts, we formulate a vector for each researcher that represents the total research output. To find researchers with similar research interests, we measure similarity between the vectors in the authors space. Top matching authors are connected visually to create a map expands over representative topics in battery related research. Figure 1 shows a snapshot of this map. Researchers names were hidden for privacy purposes. 1Priem, J., Piwowar, H., & Orr, R. (2022). OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts. ArXiv. https://arxiv.org/abs/2205.01833 2Grootendorst, M. (2020). KeyBERT: Minimal keyword extraction with BERT. https://doi.org/10.5281/zenodo.4461265 Figure 1

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.

A Combined Approach of Text Summarization using different Keyword Extraction Techniques

A Combined Approach of Text Summarization using different Keyword Extraction Techniques

An Improved Corpus-Based NLP Method for Facilitating Keyword Extraction: An Example of the COVID-19 Vaccine Hesitancy Corpus

In the current COVID-19 post-pandemic era, COVID-19 vaccine hesitancy is hindering the herd immunity generated by widespread vaccination. It is critical to identify the factors that may cause COVID-19 vaccine hesitancy, enabling the relevant authorities to propose appropriate interventions for mitigating such a phenomenon. Keyword extraction, a sub-field of natural language processing (NLP) applications, plays a vital role in modern medical informatics. When traditional corpus-based NLP methods are used to conduct keyword extraction, they only consider a word’s log-likelihood value to determine whether it is a keyword, which leaves room for concerns about the efficiency and accuracy of this keyword extraction technique. These concerns include the fact that the method is unable to (1) optimize the keyword list by the machine-based approach, (2) effectively evaluate the keyword’s importance level, and (3) integrate the variables to conduct data clustering. Thus, to address the aforementioned issues, this study integrated a machine-based word removal technique, the i10-index, and the importance–performance analysis (IPA) technique to develop an improved corpus-based NLP method for facilitating keyword extraction. The top 200 most-cited Science Citation Index (SCI) research articles discussing COVID-19 vaccine hesitancy were adopted as the target corpus for verification. The results showed that the keywords of Quadrant I (n = 98) reached the highest lexical coverage (9.81%), indicating that the proposed method successfully identified and extracted the most important keywords from the target corpus, thus achieving more domain-oriented and accurate keyword extraction results.

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.

Performance Evaluation of Keyword Extraction Techniques and Stop Word Lists on Speech-To-Text Corpus

The dawn of conversational user interfaces, through which humans communicate with computers through voice audio, has been reached. Therefore, Natural Language Processing (NLP) techniques are required to focus not only on text but also on audio speeches. Keyword Extraction is a technique to extract key phrases out of a document which can provide summaries of the document and be used in text classification. Existing keyword extraction techniques have commonly been used on only text/typed datasets. With the advent of text data from speech recognition engines which are less accurate than typed texts, the suitability of keyword extraction is questionable. This paper evaluates the suitability of conventional keyword extraction methods on a speech-to-text corpus. A new audio dataset for keyword extraction is collected using the World Wide Web (WWW) corpus. The performances of Rapid Automatic Keyword Extraction (RAKE) and TextRank are evaluated with different Stoplists on both the originally typed corpus and the corresponding Speech-To-Text (STT) corpus from the audio. Metrics of precision, recall, and F1 score was considered for the evaluation. From the obtained results, TextRank with the FOX Stoplist showed the highest performance on both the text and audio corpus, with F1 scores of 16.59% and 14.22%, respectively. Despite lagging behind text corpus, the recorded F1 score of the TextRank technique with audio corpus is significant enough for its adoption in audio conversation without much concern. However, the absence of punctuation during the STT affected the F1 score in all the techniques.

Cluster-Based Knowledge Graph and Entity-Relation Representation on Tourism Economical Sentiments

The tourism industry has experienced fast and sustainable growth over the years in the economic sector. The data available online on the ever-growing tourism sector must be given importance as it provides crucial economic insights, which can be helpful for consumers and governments. Natural language processing (NLP) techniques have traditionally been used to tackle the issues of structuring of unprocessed data, and the representation of the data in a knowledge-based system. NLP is able to capture the full richness of the text by extracting the entity and relationship from the processed data, which is gathered from various social media platforms, webpages, blogs, and other online sources, while successfully taking into consideration the semantics of the text. With the purpose of detecting connections between tourism and economy, the research aims to present a visual representation of the refined data using knowledge graphs. In this research, the data has been gathered from Twitter using keyword extraction techniques with an emphasis on tourism and economy. The research uses TextBlob to convert the tweets to numeric vector representations and further uses clustering techniques to group similar entities. A cluster-wise knowledge graph has been constructed, which comprises a large number of relationships among various factors, that visualize entities and their relationships connecting tourism and economy.

English-Learning Motivation among Chinese Mature Learners: A Comparative Study of English and Non-English Majors.

This study examined the motivation of Chinese mature students toward learning English. The participants in the study were ten Chinese mature learners, of whom four were English majors and six were non-English majors. Each underwent a semi-structured interview. Their responses were then coded and analyzed based on Matsuzaka-Carreira’s motivation framework. Furthermore, the Keyword Extraction and Link Terms techniques of PolyAnalystTM were used for further analysis. The results showed that English majors tended to be goal-oriented, and non-English majors were more likely to be means-oriented. English majors were identified as having additional integrative motivation. The factors influencing the motivation of English majors were more diverse than those influencing their non-English major counterparts. The obvious distinction between the two groups was their attitude towards the target language and culture. These findings suggest that instruction for adults should be aligned with mature learners’ practical needs and individual characteristics.

Predicting mental disorder from noisy questionnaires: an anomaly detection approach based on keyword extraction and machine learning techniques

The early warning of mental disorders is of great importance for the psychological well-being of college students. The accuracy of conventional scaling methods on questionnaires is generally low in predicting mental disorders, as the questionnaires contain much noise, and the processing on the questionnaires is rudimentary. To address this problem, we propose a novel anomaly detection framework on questionnaires, which represents each questionnaire as a document, and applies keyword extraction and machine learning techniques to detect abnormal questionnaires. We also propose a new keyword statistic for the calculation of option significance and three interpretable machine learning models for the calculation of question significance. Experiments demonstrate the effectiveness of our proposed methods.

A Comparative Evaluation of Different Keyword Extraction Techniques

Retrieving keywords in a text is attracting researchers for a long time as it forms a base for many natural language applications like information retrieval, text summarization, document categorization etc. A text is a collection of words that represent the theme of the text naturally and to bring the naturalism under certain rules is itself a challenging task. In the present paper, the authors evaluate different spatial distribution based keyword extraction methods available in the literature on three standard scientific texts. The authors choose the first few high-frequency words for evaluation to reduce the complexity as all the methods are somehow based on frequency. The authors find that the methods are not providing good results particularly in the case of the first few retrieved words. Thus, the authors propose a new measure based on frequency, inverse document frequency, variance, and Tsallis entropy. Evaluation of different methods is done on the basis of precision, recall, and F-measure. Results show that the proposed method provides improved results.

Keyword Extraction Methodologies Based on Rényi Entropy and Tsallis Relative Entropy

Textual and other forms of data is accumulating at a very fast pace through various social, academic and economic activities. Keyword extraction technique plays a significant role in analyzing, integrating, interpreting this amorphous data and exploiting its potential. Keyword extraction assists in making better sense of these vast information and data resources and leverage their value. In this article we pr opose and analyze keyword extraction methodology using Rényi entropy and Tsallis relative entropy. The proposed methods being statistics-based methods, these are language and domain independent method. The proposed methodology may find applications in dynamic text collection, information retrieval, natural language processing etc.

Word synonym relationships for text analysis: A graph-based approach.

Keyword extraction refers to the process of detecting the most relevant terms and expressions in a given text in a timely manner. In the information explosion era, keyword extraction has attracted increasing attention. The importance of keyword extraction in text summarization, text comparisons, and document categorization has led to an emphasis on graph-based keyword extraction techniques because they can capture more structural information compared to other classic text analysis methods. In this paper, we propose a simple unsupervised text mining approach that aims to extract a set of keywords from a given text and analyze its topic diversity using graph analysis tools. Initially, the text is represented as a directed graph using synonym relationships. Then, community detection and other measures are used to identify keywords in the text. The set of extracted keywords is used to assess topic diversity within the text and analyze its sentiment. The proposed approach relies on grouping semantically similar candidate words. This approach ensures that the set of extracted keywords is comprehensive. Differing from other graph-based keyword extraction approaches, the proposed method does not require user parameters during graph construction and word scoring. The proposed approach achieved significant results compared to other keyword extraction techniques.

Rank algorithm of web English educational resources based on fuzzy sets and RSS

In order to improve the acquisition and recommendation effect of English education resources, this paper proposes a Rank algorithm of web English educational resources based on fuzzy sets and RSS, and deeply studies the basic principles of the algorithm and introduces several keyword extraction techniques. The user’s browsing behavior and user interest acquisition methods are classified. Researchers can plan to further explore the page ranking algorithm to improve the performance of the scheme based on the damping factor. In addition, this paper uses Web technology to acquire English education resources and build a recommendation model, and uses crawler technology to build an overall system model. Finally, this paper designs experiments to verify the performance of the algorithm model constructed in this paper, and analyses the experimental results by mathematical statistics. The research results show that the algorithm model proposed in this paper has significant effects and is of great significance to the acquisition and recommendation of English education resources.

A survey on different dimensions for graphical keyword extraction techniques: Issues and Challenges.

The transmission from offline activities to online activities due to the social disorder evolved from COVID-19 pandemic lockdown has led to increase in the online economic and social activities. In this regard, the Automatic Keyword Extraction (AKE) from textual data has become even more interesting due to its application over different domains of Natural Language Processing (NLP). It is observed that the Graphical Keyword Extraction Techniques (GKET) use Graph of Words (GoW) in literature for analysis in different dimensions. In this article, efforts have been made to study these different dimensions for GKET, namely, the GoW representation, the statistical properties of GoW, the stability of the structure of GoW, the diversity in approaches over GoW for GKET, and the ranking of nodes in GoW. To elucidate these different dimensions, a comprehensive survey of GKET is carried in different domains to make some inferences out of the existing literature. These inferences are used to lay down possible research directions for interdisciplinary studies of network science and NLP. In addition, the experimental results are analysed to compare and contrast the existing GKET over 21 different dataset, to analyse the Word Co-occurrence Networks (WCN) for 15 different languages, and to study the structure of WCN for different genres. In this article, some strong correspondences in different disciplinary approaches are identified for different dimensions, namely, GoW representation: ’Line Graphs’ and ’Bigram Words Graphs’; Feature extraction and selection using eigenvalues: ’Random Walk’ and ’Spectral Clustering’. Different observations over the need to integrate multiple dimensions has open new research directions in the inter-disciplinary field of network science and NLP, applicable to handle streaming data and language-independent NLP.

Automation of discussion board evaluation through keyword extraction techniques: a comparative study

With the current trend in lieu of the ongoing pandemic, online learning and technology-based pedagogy receive worldwide attention. There are several pedagogical practices one can use to evaluate students mindful and the method will vary based on the learning objective. In this paper, our goal is to dive deeper into identifying the best keyword extraction technique which can be closely mapped with human evaluation. We report the results of three popular keyword extraction techniques specifically RAKE, TF-IDF, and Semantic Fingerprinting on the dataset generated from the discussion board post of a Learning Management System. The results illustrate that the TF-IDF algorithm shows the highest correlation with a human evaluation with 0.76 correlations, RAKE with 0.36, and Semantic Fingerprinting with 0.58. It was also identified Semantic Fingerprinting had a lowest mean square error of 18.26.

Study of Keyword Extraction Techniques for Electric Double‐Layer Capacitor Domain Using Text Similarity Indexes: An Experimental Analysis

Keywords perform a significant role in selecting various topic‐related documents quite easily. Topics or keywords assigned by humans or experts provide accurate information. However, this practice is quite expensive in terms of resources and time management. Hence, it is more satisfying to utilize automated keyword extraction techniques. Nevertheless, before beginning the automated process, it is necessary to check and confirm how similar expert‐provided and algorithm‐generated keywords are. This paper presents an experimental analysis of similarity scores of keywords generated by different supervised and unsupervised automated keyword extraction algorithms with expert‐provided keywords from the electric double layer capacitor (EDLC) domain. The paper also analyses which texts provide better keywords such as positive sentences or all sentences of the document. From the unsupervised algorithms, YAKE, TopicRank, MultipartiteRank, and KPMiner are employed for keyword extraction. From the supervised algorithms, KEA and WINGNUS are employed for keyword extraction. To assess the similarity of the extracted keywords with expert‐provided keywords, Jaccard, Cosine, and Cosine with word vector similarity indexes are employed in this study. The experiment shows that the MultipartiteRank keyword extraction technique measured with cosine with word vector similarity index produces the best result with 92% similarity with expert‐provided keywords. This study can help the NLP researchers working with the EDLC domain or recommender systems to select more suitable keyword extraction and similarity index calculation techniques.

Intelligent Bilingual Data Extraction and Rebuilding Using Data Mining for Big Data

In today’s World there exists various source of data in various formats (file formats), different structure, different types and etc. which is a hug collection of unstructured over the internet or social media. This gives rise to categorization of data as unstructured, semi structured and structured data. Data that exist in irregular manner without any particular schema are referred as unstructured data which is very difficult to process as it consists of irregularities and ambiguities. So, we are focused on Intelligent Processing Unit which converts unstructured big data into intelligent meaningful information. Intelligent text extraction is a technique that automatically identifies and extracts text from file format. The system consists of different stages which include the pre-processing, keyphase extraction techniques and transformation for the text extraction and retrieve structured data from unstructured data. The system consists multiple method/approach give better result. We are currently working in various file formats and converting the file format into DOCX which will come in the form of the un-structure Form, and then we will obtain that file in the structure form with the help of intelligent Pre-processing. The pre-process stages that triggers the unstructured data/corpus into structured data converting into meaning full. The Initial stage is the system remove the stop word, unwanted symbols noisy data and line spacing. The second stage is Data Extraction from various sources of file or types of files into proper format plain text. The then in third stage we transform the data or information from one format to another for the user to understand the data. The final step is rebuilding the file in its original format maintaining tag of the files. The large size files are divided into sub small size file to executed the parallel processing algorithms for fast processing of larger files and data. Parallel processing is a very important concept for text extraction and with its help; the big file breaks in a small file and improves the result. Extraction of data is done in Bilingual language, and represent the most relevant information contained in the document. Key-phase extraction is an important problem of data mining, Knowledge retrieval and natural speech processing. Keyword Extraction technique has been used to abstract keywords that exclusively recognize a document. Rebuilding is an important part of this project and we will use the entire concept in that file format and in the last, we need the same format which we have done in that file. This concept is being widely used but not much work of the work has been done in the area of developing many functionalities under one tool, so this makes us feel the requirement of such a tool which can easily and efficiently convert unstructured files into structured one.

Automatic Folder Allocation System for Electronic Text Document Repositories Using Enhanced Bayesian Classification Approach

This article proposes a system equipped with the enhanced Bayesian classification techniques to automatically assign folders to store electronic text documents. Despite computer technology advancements in the information age where electronic text files are so pervasive in information exchange, almost every single document created or downloaded from the Internet requires manual classification by the users before being deposited into a folder in a computer. Not only does such a tedious task cause inconvenience to users, the time taken to repeatedly classify and allocate a folder for each text document impedes productivity, especially when dealing with a huge number of files and deep layers of folders. In order to overcome this, a prototype system is built to evaluate the performance of the enhanced Bayesian text classifier for automatic folder allocation, by categorizing text documents based on the existing types of text documents and folders present in user's hard drive. In this article, the authors deploy a High Relevance Keyword Extraction (HRKE) technique and an Automatic Computed Document Dependent (ACDD) Weighting Factor technique to a Bayesian classifier in order to obtain better classification accuracy, while maintaining the low training cost and simple classifying processes using the conventional Bayesian approach.

Keyword Detection Techniques: A Comprehensive Study

Automatic identification of influential segments from a large amount of data is an important part of topic detection and tracking (TDT). This can be done using keyword identification via collocation techniques, word co-occurrence networks, topic modeling and other machine learning techniques. This paper reviews existing traditional keyword extraction techniques and analyzes them to make useful insights and to give future directions for better automatic, unsupervised and language independent research. The paper reviews extant literature on existing traditional TDT approaches for automatic identification of influential segments from a large amount of data in keyword detection task. The current keyword detection techniques used by researchers have been discussed. Inferences have been drawn from current keyword detection techniques used by researchers, their advantages and disadvantages over the previous studies and the analysis results have been provided in tabular form. Although keyword detection has been widely explored, there is still a large scope and need for identifying topics from the uncertain user-generated data.