knowledge graphs and large language models (LLMs) have become important tools for educational innovation. This paper explores the application of these two technologies in the construction of ideological and political ...
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knowledge in NLP has been a rising trend especially after the advent of large-scale pre-trained models. knowledge is critical to equip statistics-based models with common sense, logic and other external information. I...
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ISBN:
(纸本)9781450394079
knowledge in NLP has been a rising trend especially after the advent of large-scale pre-trained models. knowledge is critical to equip statistics-based models with common sense, logic and other external information. In this tutorial, we will introduce recent state-of-the-art works in applying knowledge in language understanding, language generation and commonsense reasoning.
CyberRwanda is a digital health intervention designed to increase knowledge of family planning and reproductive health (FP/RH) and access to youth-friendly services in Rwanda. Previous studies showed high acceptabilit...
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ISBN:
(纸本)9798350310504
CyberRwanda is a digital health intervention designed to increase knowledge of family planning and reproductive health (FP/RH) and access to youth-friendly services in Rwanda. Previous studies showed high acceptability and feasibility through user surveys, success in combating social stigma, and an effective educational impact on adolescents. In order to further enhance user experience and overcome resource limitations, the authors designed and developed new features, including an integrated dashboard and chatbot, leveraging data engineering and naturallanguageprocessing. The experiments showed that new features substantially reduce the time required to access information for user groups, potentially leading to an improved user experience.
language identification is a critical area of research within naturallanguageprocessing (NLP), particularly in multilingual contexts where accurate language detection can enhance the performance of various applicati...
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In NLG, recent research in knowledge-Grounded Text Generation aims to refine sentence specificity and naturalness. When generating texts, considering multiple turns within a conversation is considered important becaus...
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ISBN:
(纸本)9798350370027;9798350370034
In NLG, recent research in knowledge-Grounded Text Generation aims to refine sentence specificity and naturalness. When generating texts, considering multiple turns within a conversation is considered important because it allows models to generate sentences that reflect the context of the conversation. Addressing open-domain conversations, determining the optimal conversation history for training knowledge selection models lacks prior exploration. This study aims to improve KGTG models to effectively handle complex utterances by progressively incorporating more turns. This finding offers a foundational direction for boosting knowledge selection models in text generation.
Text classification is a common task in naturallanguageprocessing tasks. However, in reality, such as industrial datasets, there are numerous short texts that are ignored by existing models, in which important infor...
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ISBN:
(纸本)9798350359329;9798350359312
Text classification is a common task in naturallanguageprocessing tasks. However, in reality, such as industrial datasets, there are numerous short texts that are ignored by existing models, in which important information is inevitably missed due to the short word count. Recent studies tend to enrich the features in short text classification by introducing conceptual information. However, these simple conceptual information is also relatively sparse, with limited feature augment. In this paper, we propose Input Embedding, knowledge Base Retrieval, Large language Model Text Augmentation, and Text Encoding for the task of short text classification (IKLT). The four modules utilize the current well-performing forms of Large language Model and Retrieval Augmentation to further enrich the feature information and have interpretability. Moreover, in addition to some public datasets we establish an industrial short text dataset for comparison experiments. The experimental results show that our proposed framework for short text classification achieves improved results on all five datasets.
We investigate the extent to which Retrieval Augmented Generation improves the quality of Large language Models' answers to technical questions in the field of linguistics-a domain known for its broad terminologic...
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ISBN:
(纸本)9783031702419;9783031702426
We investigate the extent to which Retrieval Augmented Generation improves the quality of Large language Models' answers to technical questions in the field of linguistics-a domain known for its broad terminological inventory and theory-dependent use of technical terms. Furthermore, this application is not only about terminological information on language, but also about information on its well-formedness. We present the results of an empirical evaluation of automatically generated answers based on authentic data from a language consulting service, with special emphasis on different question types.
The advancements in artificial intelligence (AI) have propelled the domain of text similarity, where sophisticated algorithms harness the power of naturallanguageprocessing to analyze and compare textual content. In...
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As one of the research hotspots in naturallanguageprocessing, text sentiment analysis has received extensive attention in recent years. Aiming at the problem that the unidirectional TCN in BERT-TCN cannot fully extr...
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ISBN:
(纸本)9798331539894;9798331539887
As one of the research hotspots in naturallanguageprocessing, text sentiment analysis has received extensive attention in recent years. Aiming at the problem that the unidirectional TCN in BERT-TCN cannot fully extract the contextual semantic features of the word vectors of text, a text sentiment analysis model based on BERT and Bidirectional Temporal Convolutional Network (BiTCN) is proposed. The model first uses the BERT model to obtain the word vector representations of text, and then the word vectors are input into BiTCN to extract contextual semantic features from the forward and backward directions of the word vectors. Finally, the contextual semantic features are input into a softmax classifier for sentiment classification. Experiments show that compared with the BERT-TCN model, the BERT-BiTCN model has improved accuracy, precision, recall, and F1 value.
Requirements engineering (RE) encompasses ac-tivities such as requirements elicitation, analysis, specification, and validation, which are essential in software development for defining and aligning stakeholder needs ...
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