Assessment of Information Extraction Techniques, Models and Systems

Rahman, Atta-ur; Musleh, Dhiaa; Nabil, Majed; Alubaidan, Haya; Gollapalli, Mohammed; Krishnasamy, Gomathi; Almoqbil, Dakheel; Khan, Mohammad Aftab Alam; Farooqui, Mehwash; Ahmed, Mohammed Imran Basheer; Ahmed, Mohammed Salih; Mahmud, Maqsood

doi:10.18280/mmep.090315

Cited by 7 publications

(5 citation statements)

References 66 publications

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“…Rahman et al [26] and Zaman et al [27] presented assessment of information extraction approaches, model, and techniques by surveying more than a decade literature in the area. In this regard, they performed a comprehensive and systematic literature review.…”

Section: Literature Reviewmentioning

confidence: 99%

Rule-Based Information Extraction from Multi-format Resumes for Automated Classification

Musleh

2024

MMEP

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Nowadays, with the expansion of the Internet, a lot of people publish their resumes on the internet and social media networks. Large companies receive hundreds of resumes per day, which comes in several formats such as Joint Photographic Experts Group (JPG), Portable Document Format (PDF) and Word files. Therefore, information extraction from resumes can be applied automatically by several methods. In this research, the important details that are taken from resumes are: name, date of birth, email, phone number, GPA, gender, nationality, and address. The private resumes dataset used is taken from different sources including open source as well as personally annotated. The processes of information extraction for resumes have been performed in different phases such as: pre-processing, converting the resumes files into PDF and information extraction by the rule-based method to extract the eight elements from resumes. To carry out the experiment, the Python language is used, particularly the spacy library and word2vec technique. Consequently, the experimental results demonstrate that the testing phase achieved 96.4% information extraction precision which is quite considerable in contrast to the techniques in the literature. The scheme is then extended to classify the resume based on the extracted information fields and exhibited classification accuracy, precision, recall and F1-score as 98.02%, 98.01%, 98% and 98%, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Rule-Based Information Extraction from Multi-format Resumes for Automated Classification

Musleh

2024

MMEP

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“…A possible explanation is the more professional words typically used in the first two. It is commonly observed that precision, recall, and F-measure [69,70] are among the most widely used criteria to evaluate the performance of deep learning approaches as shown in the last rows of Table 4.…”

Section: Deep Learning-based Approachesmentioning

confidence: 99%

Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes

et al. 2023

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Over the decades, a tremendous increase has been witnessed in the production of documents available in digital form. The increased production of documents has gained so much momentum that their rate of production jumps two-fold every five years. These articles are searched over the internet via search engines, digital libraries, and citation indexes. However, the retrieval of relevant research papers for user queries is still a pipedream. This is because scientific documents are not indexed based on some subject classification hierarchies. Hence, the classification of these documents becomes a challenging task for the researchers. Classification of the documents can be two-fold: one way is to assign a single label to each document and the other is to assign multi-labels to each document based on its belonging domains. Classification of the documents can be performed by using either the available metadata or the whole content of the documents. While performing classification, there are many challenges which may belong to the dataset, feature selection technique, preprocessing methodology, and which classification model is suitable for the classification of the documents. This paper highlights the issues for single-label and multi-label classification by using either metadata or content of the documents and why metadata-based approaches are better than content-based approaches in terms of feasibility.

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“…The moment these mentioned set of signals are transmitted over a frequency nonselective Rayleigh flat fading channel as utilized in several studies in the literature for similar type of the assumed OFDM systems [41][42][43][44][45][46][47][48], the received equivalent low pass signal can be written as:…”

Section: Channelmentioning

confidence: 99%

Enhancing Multi-User Detection in Multicarrier 5G and Beyond: A Space-Time Spreading Approach with Parallel Interference Cancellation

Aljameel,

Rahman

2023

MMEP

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This study explores a composite space-time and frequency-domain spreading strategy, designed to augment the capacity of multicarrier 5G systems operating over frequencyselective Rayleigh fading channels. The focus is directed towards a comprehensive analysis of the Bit Error Rate (BER) performance of the proposed system, with adjustments made to various parametric values. In tandem, receiver optimization techniques are meticulously studied, and their outcomes are positioned against existing literature. Within this context, the Parallel Interference Canceller (PIC) emerges as a viable alternative to the De-correlating Detector (DD), a shift primarily driven by the latter's heightened complexity and noise amplification. Additionally, this study demonstrates the acquisition of a larger number of users exclusively employing transmission diversity, thereby eliminating the need for receiving diversity and additional code sets. This approach incrementally augments hardware complexity at both ends of the transmission link, a minor trade-off for the benefits garnered. The efficacy of this scheme is substantiated through MATLAB simulations, indicating a promising avenue for improving the capacity of multicarrier 5G systems. The findings pave the way for significant advancements in the development of efficient and robust communication systems for the 5G era and beyond.

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Assessment of Information Extraction Techniques, Models and Systems

Cited by 7 publications

References 66 publications

Rule-Based Information Extraction from Multi-format Resumes for Automated Classification

Rule-Based Information Extraction from Multi-format Resumes for Automated Classification

Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes

Enhancing Multi-User Detection in Multicarrier 5G and Beyond: A Space-Time Spreading Approach with Parallel Interference Cancellation

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