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Intent classification is essential in Natural Language Processing, serving applications like virtual assistants and customer service by categorizing user inputs into predefined classes. Despite its importance, the effectiveness of intent classifiers is often constrained by the scarcity of labeled data, as acquiring substantial, annotated datasets is costly and impractical. Data augmentation addresses this by expanding datasets with modified or synthetic examples, a common practice in computer vision but more complex in NLP due to the discrete nature of language. Traditional NLP data augmentation methods have been explored but exhibit limitations. This paper investigates the use of Large Language Models for generating labeled data to enhance intent classification. We explore whether LLM-generated data can effectively augment training sets, comparing its impact on intent classifier performance against traditional augmentation methods. Our study reveals that LLMs can generate diverse and realistic data, potentially improving classifier accuracy in low-data scenarios, thereby providing valuable insights into leveraging generative AI for NLP tasks in real-world applications.

Recently proposed legal frameworks for Artificial Intelligence (AI) depart from some frameworks of concepts regarding ethical and trustworthy AI that provide the technical grounding for safety and risk. This is especially important in high-risk applications, such as those involved in decision-making support systems in the biomedical domain. Frameworks for trustworthy AI span diverse requirements, including human agency and oversight, technical robustness and safety, privacy and data governance, transparency, fairness, and societal and environmental impact. Researchers and practitioners who aim to transition experimental AI models and software to the market as medical devices or to use them in actual medical practice face the challenge of deploying processes, best practices, and controls that are conducive to complying with trustworthy AI requirements. While checklists and general guidelines have been proposed for that aim, a gap exists between the frameworks and the actual practices. This paper reports the first scoping review on the topic that is specific to decision-making systems in the biomedical domain and attempts to consolidate existing practices as they appear in the academic literature on the subject.

Conversational Agents (CA) are increasingly being deployed by organizations to provide round-the-clock support and to increase customer satisfaction. All CA have one thing in common despite the differences in their design: they need to be trained with users’ intents and corresponding training sentences. Access to proper data with acceptable coverage of intents and training sentences is a big challenge in CA deployment. Even with the access to the past conversations, the process of discovering intents and training sentences manually is not time and cost-effective. Here, an end to end automated framework that can discover intents and their training sentences in conversation logs to generate labeled data sets for training intent models is presented. The framework proposes different feature engineering techniques and leverages dimensionality reduction methods to assemble the features, then applies a density-based clustering algorithm iteratively to mine even the least common intents. Finally, the clustering results are automatically labeled by the final algorithm.

Intent classification is essential in Natural Language Processing, serving applications like virtual assistants and customer service by categorizing user inputs into predefined classes. Despite its importance, the effectiveness of intent classifiers is often constrained by the scarcity of labeled data, as acquiring substantial, annotated datasets is costly and impractical. Data augmentation addresses this by expanding datasets with modified or synthetic examples, a common practice in computer vision but more complex in NLP due to the discrete nature of language. Traditional NLP data augmentation methods have been explored but exhibit limitations. This paper investigates the use of Large Language Models for generating labeled data to enhance intent classification. We explore whether LLM-generated data can effectively augment training sets, comparing its impact on intent classifier performance against traditional augmentation methods. Our study reveals that LLMs can generate diverse and realistic data, potentially improving classifier accuracy in low-data scenarios, thereby providing valuable insights into leveraging generative AI for NLP tasks in real-world applications. [ABSTRACT FROM AUTHOR]

Recently proposed legal frameworks for Artificial Intelligence (AI) depart from some frameworks of concepts regarding ethical and trustworthy AI that provide the technical grounding for safety and risk. This is especially important in high-risk applications, such as those involved in decision-making support systems in the biomedical domain. Frameworks for trustworthy AI span diverse requirements, including human agency and oversight, technical robustness and safety, privacy and data governance, transparency, fairness, and societal and environmental impact. Researchers and practitioners who aim to transition experimental AI models and software to the market as medical devices or to use them in actual medical practice face the challenge of deploying processes, best practices, and controls that are conducive to complying with trustworthy AI requirements. While checklists and general guidelines have been proposed for that aim, a gap exists between the frameworks and the actual practices. This paper reports the first scoping review on the topic that is specific to decision-making systems in the biomedical domain and attempts to consolidate existing practices as they appear in the academic literature on the subject. [ABSTRACT FROM AUTHOR]

Twitter is undoubtedly one of the most widely used data sources to analyze human communication. The literature is full of examples where Twitter is accessed, and data are downloaded as the previous step to a more in-depth analysis in a wide variety of knowledge areas. Unfortunately, the extraction of relevant information from the opinions that users freely express in Twitter is complicated, both because of the volume generated—more than 6000 tweets per second—and the difficulties related to filtering out only what is pertinent to our research. Inspired by the fact that a large part of users use Twitter to communicate or receive political information, we created a method that allows for the monitoring of a set of users (which we will call authorities) and the tracking of the information published by them about an event. Our approach consists of dynamically and automatically monitoring the hottest topics among all the conversations where the authorities are involved, and retrieving the tweets in connection with those topics, filtering other conversations out. Although our case study involves the method being applied to the political discussions held during the Spanish general, local, and European elections of April/May 2019, the method is equally applicable to many other contexts, such as sporting events, marketing campaigns, or health crises.