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Natural Language Processing (NLP) is a branch of computer science, artificial intelligence, information engineering, and linguistics that focuses on enabling machines to understand, interpret, and generate human language. Machine translation, sentiment analysis, speech recognition, and questionanswering systems are just a few examples of NLP applications. NLP has advanced rapidly in recent years, owing to the availability of large amounts of text data and the development of deep learning models, which has resulted in increased accuracy and efficiency in NLP tasks. This paper tackles the state of the art of NLP, its methodology and techniques, and its applications in different fields. The paper also discusses the role of NLP to improve education. [ABSTRACT FROM AUTHOR]

Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine capable of responding in a manner similar to human intelligence. Research in this area includes robotics, language recognition, image identification, natural language processing, and expert systems. In recent years, the availability of large datasets, the development of effective algorithms, and access to powerful computers have led to unprecedented success in artificial intelligence. This powerful tool has been used in numerous scientific and engineering fields including mineral identification. This paper summarizes the methods and techniques of artificial intelligence applied to intelligent mineral identification based on research, classifying the methods and techniques as artificial neural networks, machine learning, and deep learning. On this basis, visualization analysis is conducted for mineral identification of artificial intelligence from field development paths, research hot spots, and keywords detection, respectively. In the end, based on trend analysis and keyword analysis, we propose possible future research directions for intelligent mineral identification. [ABSTRACT FROM AUTHOR]

Recently, systems have become more flexible and smarter in their implementation and functions. Post-processing involves automatic post editing and the application of various strategies during decoding in the translation process. Park et al. propose a software that can be applied to natural language processing (NLP) by way of pre-processing and post-processing. The purpose of these papers is to share research ideas surrounding algorithm development and data processing and analysis for smart systems. [Extracted from the article]

The explosive growth of the social media community has increased many kinds of misinformation and is attracting tremendous attention from the research community. One of the most prevalent ways of misleading news is cheapfakes. Cheapfakes utilize non-AI techniques such as unaltered images with false context news to create false news, which makes it easy and "cheap" to create and leads to an abundant amount in the social media community. Moreover, the development of deep learning also opens and invents many domains relevant to news such as fake news detection, rumour detection, fact-checking, and verification of claimed images. Nevertheless, despite the impact on and harmfulness of cheapfakes for the social community and the real world, there is little research on detecting cheapfakes in the computer science domain. It is challenging to detect misused/false/out-of-context pairs of images and captions, even with human effort, because of the complex correlation between the attached image and the veracity of the caption content. Existing research focuses mostly on training and evaluating on given dataset, which makes the proposal limited in terms of categories, semantics and situations based on the characteristics of the dataset. In this paper, to address these issues, we aimed to leverage textual semantics understanding from the large corpus and integrated with different combinations of text-image matching and image captioning methods via ANN/Transformer boosting schema to classify a triple of (image, caption1, caption2) into OOC (out-of-context) and NOOC (no out-of-context) labels. We customized these combinations according to various exceptional cases that we observed during data analysis. We evaluate our approach using the dataset and evaluation metrics provided by the COSMOS baseline. Compared to other methods, including the baseline, our method achieves the highest Accuracy, Recall, and F1 scores. [ABSTRACT FROM AUTHOR]

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The recent years have witnessed a rapid increase in the number of scientific articles in biomedical domain. These literature are mostly available and readily accessible in electronic format. The domain knowledge hidden in them is critical for biomedical research and applications, which makes biomedical literature mining (BLM) techniques highly demanding. Numerous efforts have been made on this topic from both biomedical informatics (BMI) and computer science (CS) communities. The BMI community focuses more on the concrete application problems and thus prefer more interpretable and descriptive methods, while the CS community chases more on superior performance and generalization ability, thus more sophisticated and universal models are developed. The goal of this paper is to provide a review of the recent advances in BLM from both communities and inspire new research directions. [ABSTRACT FROM AUTHOR]

In natural language processing (NLP), transformer based models have surpassed recurrent neural networks (RNN) as state of the art, being introduced specifically to address the limitations of RNNs originating from its sequential nature. As a similar sequence modeling problem, transformer methods can be readily adapted for deep learning time series prediction. This paper proposes a sparse transformer based approach for electricity load prediction. The layers of a transformer addresses the shortcomings of RNNs and CNNs by applying the attention mechanism on the entire time series, allowing any data point in the input to influence any location in the output of the layer. This allows transformers to incorporate information from the entire sequence in a single layer. Attention computations can also be parallelized. Thus, transformers can achieve faster speeds, or trade this speed for more layers and increased complexity. In experiments on public datasets, the sparse transformer attained comparable accuracy to an RNN-based SOTA method (Liu et al., 2022) while being up to 5× faster during inference. Moreover, the proposed model is general enough to forecast the load from individual households to city levels as shown in the extensive experiments conducted. [ABSTRACT FROM AUTHOR]

In the paper is in short presented the progress made in Speech Technology during the long existence of more than 70 years of this mankind activity. Starting as an exclusivist scientific research domain around 1950, it reached the level of major scientific interest research domain in the 70 years of the precedent century, taking advantage of the enormous progress in electronic integrated circuit technology (ICT), digital signal processing (DSP), computer science (CS) machine learning (ML) and artificial intelligence (AI). Around 1980, as oral form of language, speech research was in time associated with text processing, conducting to natural language processing (NLP). The huge work done in this connected domains leads in the 90 years to first applications in the real life of the automatic speech recognition and understanding (ASRU) and of the speech synthesis in the form of text to speech (TTS) systems. It will be also highlighted the new challenges for the research, in form of concepts like deep learning (DL) or big data (BD), with their capability to innovate in the domain. Finally some of the large spectrum of useful applications available today will be discussed, showing that it can be said that speech technology became a part of our daily life. [ABSTRACT FROM AUTHOR]

Significant progress has been made in applying deep learning on natural language processing tasks recently. However, deep learning models typically require a large amount of annotated training data while often only small labeled datasets are available for many natural language processing tasks in biomedical literature. Building large-size datasets for deep learning is expensive since it involves considerable human effort and usually requires domain expertise in specialized fields. In this work, we consider augmenting manually annotated data with large amounts of data using distant supervision. However, data obtained by distant supervision is often noisy, we first apply some heuristics to remove some of the incorrect annotations. Then using methods inspired from transfer learning, we show that the resulting models outperform models trained on the original manually annotated sets. [ABSTRACT FROM AUTHOR]

2021; 2021(5)12, 6661035, 10.1155/2021/6661035 12 Cao H., Li X., Lian S., Zhan C. A hotspot information extraction hybrid solution of online posts' textual data. From a medical perspective, textual information posted in social networks could be an important thermometer to both detect and suggest medical diseases/treatments and measure the quality of healthcare services. Over the last decade, the explosive growth of social media and instant message applications together with the improvement of computer performance, networking infrastructures, and storage capabilities have led to the advent of the information age. [Extracted from the article]