The automatic development of meaningful, detailed textual descriptions for supplied images is a difficult task in the fields of computer vision and naturallanguageprocessing. As a result, an AI-powered image caption...
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The automatic development of meaningful, detailed textual descriptions for supplied images is a difficult task in the fields of computer vision and naturallanguageprocessing. As a result, an AI-powered image caption generator can be incredibly useful for producing captions. In this study, we present a unique method for creating picture captions utilizing an attention mechanism that concentrates on pertinent areas of the image while it creates captions. On benchmark datasets, our model, which uses deep neural networks to extract picture attributes and produce captions, obtains state-of-the-art results, confirming the effectiveness of the attention mechanism in raising the caliber of the generated captions. We also offer a thorough evaluation of the performance of our approach and talk about potential future directions for enhancing image caption generation.
This paper introduces a novel approach that leverages interactions with a chatbot for assessing the progression of mild cognitive impairment while at the same time aiding users in their daily tasks. Given the potentia...
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This paper presents the EEMWT dataset, a collection of 1873 triplets of co-hyponymic multiword terms of the electrical engineering domain. Each triplet combines an anchor term with closely and distantly related co-hyp...
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Word-level completion can automatically complete words as the translator types character sequences. Word-level completion can accelerate the editing process of human translation and ensure the translation quality. Alt...
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ISBN:
(纸本)9789819794393;9789819794409
Word-level completion can automatically complete words as the translator types character sequences. Word-level completion can accelerate the editing process of human translation and ensure the translation quality. Although significant progress has been made in the field, there may be multiple candidate words when models predict words. Multiple words make up a list of candidate words. We improve the existing model by determining the most credible word in the candidate word list. We propose a multi-model fusion method to increase the accuracy of word-level completion. The improved model can use multiple evaluation criteria (Lesk method, WordNet knowledge base, and pre-training model) to calculate the scores of words by classification and weighting. The word with the highest score is selected as the most credible word. The experimental results prove that our proposed method is effective. In De-.En, our method improves the accuracy by 2.83%. In Zh-.En, our method improves the accuracy by 2.77%.
Text-to-speech (TTS) turns written text into spoken words using artificial voices. This uses naturallanguageprocessing (NLP) and speech synthesis to make audio from text input. TTS has many uses - for people with vi...
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Information retrieval (IR) is a topic of continuing study, with the apparent goal being to find the most relevant data in massive repositories. The user's inquiry is essential at this stage. Finding crucial docume...
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Recurrent neural networks (RNNs) are becoming increasingly popular in addressing naturallanguageknowledge duties. It is because of their ability to capture lengthy-time period dependencies and collection shapes by l...
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Visual Chinese Character Checking (C3) aims to detect and correct errors in handwritten Chinese text images, including faked characters and misspelled characters. This task is beneficial for subsequent tasks by improv...
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ISBN:
(纸本)9789819794423;9789819794430
Visual Chinese Character Checking (C3) aims to detect and correct errors in handwritten Chinese text images, including faked characters and misspelled characters. This task is beneficial for subsequent tasks by improving the efficiency of identifying errors in handwritten text. Recent methods are mainly based on Optical Character Recognition (OCR) and Pre-trained language Models (PLMs). Visual Chinese Character Checking is an emerging task, and relevant research has made progress. However, we believe that existing work has not fully leveraged the inherent knowledge of pre-trained models and has not addressed the semantic bias issue between pre-trained models and the character checking task. These challenges result in deficiencies in recognizing misspelled Chinese characters and correcting misused characters. Therefore, we propose various multimodal contrastive learning methods based on image-to-image and image-to-text comparisons. These methods are used throughout the processes of character recognition, error detection, and correction. By aligning the semantic feature representations among different models, our approach makes these models more suitable for the Visual Chinese Character Checking task, thereby enhancing their capabilities.
knowledge base question answering (KBQA) is designed to respond to naturallanguage inquiries by utilizing factual information, such as entities, relationships, and attributes, derived from a knowledge base (KB). The ...
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ISBN:
(纸本)9798350381641
knowledge base question answering (KBQA) is designed to respond to naturallanguage inquiries by utilizing factual information, such as entities, relationships, and attributes, derived from a knowledge base (KB). The advent of large language models (LLMs) has significantly boosted the performance of KBQA, owing to their exceptional capabilities in content comprehension and generation. In this paper, we present a knowledge Ocean enhanced Salary Analytics (KOSA) system based on knowledge graphs and LLMs tailored to employee salary data from a public university. This system encompasses an interactive conversational interface, visualization of knowledge graphs, and advanced data analysis. By employing the framework of knowledgeengineering, we enable knowledge graph modeling, Cypher (the query engine of Neo4j) reasoning, and question answering functionalities. Furthermore, machine learning algorithms are integrated to facilitate advanced features, such as salary prediction and allocation.
naturallanguageprocessing (NLP) stands at the forefront of artificial intelligence, empowering machines with human-like capabilities to process text and speech. The versatile applications of NLP encompass informatio...
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