Automated Essay Scoring is one of the most important educational applications of naturallanguageprocessing. It helps teachers with automatic assessments, providing a cheaper, faster, and more deterministic approach ...
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ISBN:
(纸本)9783031790348;9783031790355
Automated Essay Scoring is one of the most important educational applications of naturallanguageprocessing. It helps teachers with automatic assessments, providing a cheaper, faster, and more deterministic approach than humans when scoring essays. Nevertheless, off-topic essays pose challenges in this area, causing an automated grader to overestimate the score of an essay that does not adhere to a proposed topic. Thus, detecting off-topic essays is important for dealing with unrelated text responses to a given topic. This paper explored approaches based on handcrafted features to feed supervised machine-learning algorithms, tuning a BERT model, and prompt engineering with a large language model. We assessed these strategies in a public corpus of Portuguese essays, achieving the best result using a fine-tuned BERT model with a 75% balanced accuracy. Furthermore, this strategy was able to identify low-quality essays.
Zero-shot event-relational reasoning is an important task in naturallanguageprocessing, and existing methods jointly learn a variety of event-relational prefixes and inference-form prefixes to achieve such tasks. Ho...
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In the digital age, the recruitment landscape has changed dramatically. This study aims to optimize recruitment by automating resume screening through advanced naturallanguageprocessing (NLP) and Machine Learning (M...
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This paper examines the challenges and opportunities in teaching naturallanguageprocessing (NLP) at the undergraduate level, particularly in the era of Large language Models (LLMs). It discusses the diverse backgrou...
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ISBN:
(纸本)9783031843907;9783031843914
This paper examines the challenges and opportunities in teaching naturallanguageprocessing (NLP) at the undergraduate level, particularly in the era of Large language Models (LLMs). It discusses the diverse backgrounds of students, including those from computer science and non-computer science disciplines, and the need to incorporate Indian languages into the curriculum. The study identifies key challenges, such as integrating classical NLP methods with contemporary deep learning techniques, and outlines practical issues in syllabus design. By focusing on the unique context of Indian universities, this paper offers actionable recommendations for educators to enhance NLP education. The insights and strategies proposed aim to address the evolving landscape of NLP and prepare students for the demands of the field.
Socially interactive agents are gaining prominence in domains like healthcare, education, and service contexts, particularly virtual agents due to their inherent scalability. To facilitate authentic interactions, thes...
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naturallanguageprocessing (NLP) methods can annotate free-text radiology reports to create large datasets at the scale of an entire health system or beyond. Generalizing the disease classification across multiple or...
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ISBN:
(纸本)9781510686007;9781510686014
naturallanguageprocessing (NLP) methods can annotate free-text radiology reports to create large datasets at the scale of an entire health system or beyond. Generalizing the disease classification across multiple organ systems inherently requires a complex, robust, and accurate classification model. Concurrently, NLP methods have significantly improved and become more sophisticated. This study compares two traditional NLP methods, a rule-based algorithm (RBA) and a Bidirectional Long Short-Term Memory network (BiLSTM), with a lightweight variant of the Large language Model Meta AI (Llama) model. Our goal is to analyze the capabilities and limitations of each model in accurately classifying diseases encountered within the chest, abdominal, and pelvic computed tomography (CT) exams of the body. Rule-based algorithms (RBAs) were used to extract disease labels from the "findings" section of CT radiology reports, creating the training, validation, and testing datasets. Disease labels were made for three organ systems: the lungs/pleura, liver/gallbladder, and kidneys/ureters. A BiLSTM network with an attention mechanism was trained on 151,431 cases and tested on 85,987 cases. The BiLSTM and Meta's Llama3.1-8B model was evaluated on the RBA-test set and a manually annotated dataset. On the smaller, manually labeled test set, the RBA model achieved the highest macro F1 score (0.94), followed by the BiLSTM (0.91) and then Llama (0.89). In contrast, on the larger RBA-labeled test set, the BiLSTM maintained high performance (average AUC > 0.98;macro F1 = 0.95), while Llama's macro F1 dropped to 0.65. Manual spot checking of reports where Llama disagreed with RBA/BiLSTM revealed numerous instances in which Llama was actually correct, indicating flaws with the previous RBA labeling. This study emphasizes the limitations of rule-based approaches and the need to consider clinical context in ambiguous scenarios. Llama3.1-8B exhibits the potential to outperform rule-based methods,
Large language Models (LLMs) have demonstrated exceptional performance across a broad spectrum of cross-lingual naturallanguageprocessing (NLP) tasks. However, previous methods predominantly focus on leveraging para...
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Considering the rapidly expanding area of textual data, extracting meaningful insights is a significant challenge. With our novel method for automatic keyphrase extraction, which integrates naturallanguageprocessing...
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Pre-trained language models (PrLMs) demonstrate impressive performance on the sentiment analysis task. However, the large number of trainable parameters brings about heavy computational costs, which become more seriou...
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Night unmanned aerial vehicle (UAV) tracking is impeded by the challenges of poor illumination, with previous daylight-optimized methods demonstrating suboptimal performance in low-light conditions, limiting the utili...
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