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The enormous growth in online research publications in diversified domains has attracted the research community to extract these valuable scientific resources by searching online digital libraries and publishers' websites. A precise search is desired to enlist most related articles by applying semantic queries to the document's metadata and the structural elements. The online search engines and digital libraries offer only keyword-based search on full-body text, which creates excessive results. Therefore, the research article's structural and metadata information has to be stored in machine comprehendible form by the online research publishers. The research community in recent years has adopted different approaches to extract structural information from research documents like rule-based heuristics and machine-learning-based approaches. Studies suggest that machine-learning-based techniques have produced optimum results for document structure extraction from publishers having diversified publication layouts. In this paper, we have proposed thirteen different logical layout structural (LLS) components. We have identified a two-staged innovative set of generic features that are associated with the LLS. This approach has given our technique an advantage against the state-of-the-art for structural classification of digital scientific articles with diversified publication styles. We have applied chi-square ( c h i 2 ) for feature selection, and the final result has revealed that SVM (Kernal function) has produced an optimum result with an overall F-measure of 0.95. [ABSTRACT FROM AUTHOR]

This article presents a study on the multi-class classification of job postings using machine learning algorithms. With the growth of online job platforms, there has been an influx of labor market data. Machine learning, particularly NLP, is increasingly used to analyze and classify job postings. However, the effectiveness of these algorithms largely hinges on the quality and volume of the training data. In our study, we propose a multi-class classification methodology for job postings, drawing on AI models such as text-davinci-003 and the quantized versions of Falcon 7b (Falcon), Wizardlm 7B (Wizardlm), and Vicuna 7B (Vicuna) to generate synthetic datasets. These synthetic data are employed in two use-case scenarios: (a) exclusively as training datasets composed of synthetic job postings (situations where no real data is available) and (b) as an augmentation method to bolster underrepresented job title categories. To evaluate our proposed method, we relied on two well-established approaches: the feedforward neural network (FFNN) and the BERT model. Both the use cases and training methods were assessed against a genuine job posting dataset to gauge classification accuracy. Our experiments substantiated the benefits of using synthetic data to enhance job posting classification. In the first scenario, the models' performance matched, and occasionally exceeded, that of the real data. In the second scenario, the augmented classes consistently outperformed in most instances. This research confirms that AI-generated datasets can enhance the efficacy of NLP algorithms, especially in the domain of multi-class classification job postings. While data augmentation can boost model generalization, its impact varies. It is especially beneficial for simpler models like FNN. BERT, due to its context-aware architecture, also benefits from augmentation but sees limited improvement. Selecting the right type and amount of augmentation is essential. [ABSTRACT FROM AUTHOR]

In this article, we investigate the potential of synthetic resumes as a means for the rapid generation of training data and their effectiveness in data augmentation, especially in categories marked by sparse samples. The widespread implementation of machine learning algorithms in natural language processing (NLP) has notably streamlined the resume classification process, delivering time and cost efficiencies for hiring organizations. However, the performance of these algorithms depends on the abundance of training data. While selecting the right model architecture is essential, it is also crucial to ensure the availability of a robust, well-curated dataset. For many categories in the job market, data sparsity remains a challenge. To deal with this challenge, we employed the OpenAI API to generate both structured and unstructured resumes tailored to specific criteria. These synthetically generated resumes were cleaned, preprocessed and then utilized to train two distinct models: a transformer model (BERT) and a feedforward neural network (FFNN) that incorporated Universal Sentence Encoder 4 (USE4) embeddings. While both models were evaluated on the multiclass classification task of resumes, when trained on an augmented dataset containing 60 percent real data (from Indeed website) and 40 percent synthetic data from ChatGPT, the transformer model presented exceptional accuracy. The FFNN, albeit predictably, achieved lower accuracy. These findings highlight the value of augmented real-world data with ChatGPT-generated synthetic resumes, especially in the context of limited training data. The suitability of the BERT model for such classification tasks further reinforces this narrative. [ABSTRACT FROM AUTHOR]

The immense growth in online research publications has attracted the research community to extract valuable information from scientific resources by exploring online digital libraries and publishers' websites. The metadata stored in a machine comprehendible form can facilitate a precise search to enlist most related articles by applying semantic queries to the document's metadata and the structural elements. The online search engines and digital libraries offer only keyword-based search on full-body text, which creates excessive results. The research community in recent years has adopted different approaches to extract structural information from research documents. We have distributed the content of an article into two logical layouts and metadata levels. This strategy has given our technique an advantage over the state-of-the-art (SOTA) extracting metadata with diversified publication styles. The experimental results have revealed that the proposed approach has shown a significant gain in performance of 20.26% to 27.14%. [ABSTRACT FROM AUTHOR]

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The unprecedented growth of the research publications in diversified domains has overwhelmed the research community. It requires a cumbersome process to extract this enormous information by manually analyzing these research documents. To automatically extract content of a document in a structured way, metadata and content must be annotated. Scientific community has been focusing on automatic extraction of content by forming different heuristics and applying different machine learning techniques. One of the renowned conference organizers, ESWC organizes state-of-the-art challenge to extract metadata like authors, affiliations, countries in affiliations, supplementary material, sections, table, figures, funding agencies, and EU funded projects from PDF files of research articles. We have proposed a feature centric technique that can be used to extract logical layout structure of articles from publishers with diversified composition styles. To extract unique metadata from a research article placed in logical layout structure, we have developed a four-staged novel approach “FLAG-PDFe”. The approach is built upon distinct and generic features based on the textual and the geometric information from the raw content of research documents. At the first stage, the distinct features are used to identify different physical layout components of an individual article. Since research journals follow their unique publishing styles and layout formats, therefore, we develop generic features to handle these diversified publishing patterns. We employ support vector classification (SVC) in the third stage to extract the logical layout structure (LLS)/ sections of an article, after performing comprehensive evaluation of generic features and machine learning models. Finally, we further apply heuristics on LLS to extract the desired metadata of an article. The outcomes of the study are obtained using the gold standard data set. The results yields 0.877 recall, precision 0.928 and 0.897 F-measure. Our approach has achieved a 16% gain on f-measure when compared to the best approach of the ESWC challenge.

In last few decades, with the advent of World Wide Web (WWW), world is being overloaded with huge data. This huge data carries potential information that once extracted, can be used for betterment of humanity. Information from this data can be extracted using manual and automatic analysis. Manual analysis is not scalable and efficient, whereas, the automatic analysis involves computing mechanisms that aid in automatic information extraction over huge amount of data. WWW has also affected overall growth in scientific literature that makes the process of literature review quite laborious, time consuming and cumbersome job for researchers. Hence a dire need is felt to automatically extract potential information out of immense set of scientific articles to automate the process of literature review. Therefore, in this study, aim is to present the overall progress concerning automatic information extraction from scientific articles. The information insights extracted from scientific articles are classified in two broad categories i.e. metadata and key-insights. As available benchmark datasets carry a significant role in overall development in this research domain, existing datasets against both categories are extensively reviewed. Later, research studies in literature that have applied various computational approaches applied on these datasets are consolidated. Major computational approaches in this regard include Rule-based approaches, Hidden Markov Models, Conditional Random Fields, Support Vector Machines, Naïve-Bayes classification and Deep Learning approaches. Currently, there are multiple projects going on that are focused towards the dataset construction tailored to specific information needs from scientific articles. Hence, in this study, state-of-the-art regarding information extraction from scientific articles is covered. This study also consolidates evolving datasets as well as various toolkits and code-bases that can be used for information extraction from scientific articles. [ABSTRACT FROM AUTHOR]

Secondary-school teachers are in constant need of finding relevant digital resources to support specific didactic goals. Unfortunately, generic search engines do not allow them to identify learning objects among semi-structured candidate educational resources, much less retrieve them by teaching goals. This article describes a multi-strategy approach for semantically guided extraction, indexing and search of educational metadata; it combines machine learning, concept analysis, and corpus-based natural language processing techniques. The overall model was validated by comparing extracted metadata against standard search methods and heuristic-based techniques for Classification Accuracy and Metadata Quality (as evaluated by actual teachers), yielding promising results and showing that this semantically guided metadata extraction can effectively enhance access and use of educational digital material. [ABSTRACT FROM AUTHOR]

The continuous development of the Linked Data Web depends on the advancement of the underlying extraction mechanisms. This is of particular interest for the scientific publishing domain, where currently most of the data sets are being created manually. In this article, we present a Machine Learning pipeline that enables the automatic extraction of heading metadata (i.e., title, authors, etc) from scientific publications. The experimental evaluation shows that our solution handles very well any type of publication format and improves the average extraction performance of the state of the art with around 4%, in addition to showing an increased versatility. Finally, we propose a flexible Linked Data-driven mechanism to be used both for refining and linking the automatically extracted metadata. [ABSTRACT FROM AUTHOR]