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Topic modeling techniques are popularly used for document clustering, large-scale text analysis, information extraction from unstructured text documents, feature selection from large corpus, and various recommendation systems. This work suggested a framework using topic modeling techniques for legal information extraction from the Indian judicial system's unstructured legal judgments. The suggested approach aims to eliminate time-consuming manual judgment analysis in favor of automated judgment analysis that can quickly examine large number of judgments in reduced time span. In this work, we have experimented with different topic modeling methodologies for information extraction. The proposed framework is built on the Latent Dirichlet Allocation, to categorize legal judgments into extracted topic groups. Indian Supreme Court judgements are considered for the experimental setting. The three main elements of the framework are pre-processing, applying the topic model, and model evaluation using a coherence score metric. The framework was successfully applied to a corpus size of 100, 500, and 1000 legal judgments in batches. The proposed framework is used to measure legal judgment similarity to demonstrate its quantitative evaluation. In the future scope, various legal tasks that can benefit from the proposed framework for performance improvement are suggested. [ABSTRACT FROM AUTHOR]

In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are experts in different domains when a potential user requests their expertise. IR-based methods construct multifaceted textual profiles for each expert by clustering information from their scientific publications. Several methods fully tailored for this problem are presented in this paper. In contrast, ML-based methods treat expert finding as a classification task, training automatic text classifiers using publications authored by experts. By comparing these approaches, we contribute to a deeper understanding of academic-expert-finding techniques and their applicability in knowledge discovery. These methods are tested with two large datasets from the biomedical field: PMSC-UGR and CORD-19. The results show how IR techniques were, in general, more robust with both datasets and more suitable than the ML-based ones, with some exceptions showing good performance. [ABSTRACT FROM AUTHOR]

Aim/Purpose This study aims to evaluate the success of deep learning in building user profiles for personalized information access. Background To better express document content and information during the matching phase of the information retrieval (IR) process, deep learning architectures could potentially offer a feasible and optimal alternative to user profile building for personalized information access. Methodology This study uses deep learning-based models to deduce the domain of the document deemed implicitly relevant by a user that corresponds to their center of interest, and then used predicted domain by the best given architecture with user's characteristics to predict other centers of interest. Contribution This study contributes to the literature by considering the difference in vocabulary used to express document content and information needs. Users are integrated into all research phases in order to provide them with relevant information adapted to their context and their preferences meeting their precise needs. To better express document content and information during this phase, deep learning models are employed to learn complex representations of documents and queries. These models can capture hierarchical, sequential, or attention-based patterns in textual data. Findings The results show that deep learning models were highly effective for building user profiles for personalized information access since they leveraged the power of neural networks in analyzing and understanding complex patterns in user behavior, preferences, and user interactions. Recommendations for Practitioners Building effective user profiles for personalized information access is an ongoing process that requires a combination of technology, user engagement, and a commitment to privacy and security. Recommendations for Researchers Researchers involved in building user profiles for personalized information access play a crucial role in advancing the field and developing more innovative deep-based networks solutions by exploring novel data sources, such as biometric data, sentiment analysis, or physiological signals, to enhance user profiles. They can investigate the integration of multimodal data for a more comprehensive understanding of user preferences. Impact on Society The proposed models can provide companies with an alternative and sophisticated recommendation system to foster progress in building user profiles by analyzing complex user behavior, preferences, and interactions, leading to more effective and dynamic content suggestions. Future Research The development of user profile evolution models and their integration into a personalized information search system may be confronted with other problems such as the interpretability and transparency of the learning-based models. Developing interpretable machine learning techniques and visualization tools to explain how user profiles are constructed and used for personalized information access seems necessary to us as a future extension of our work. [ABSTRACT FROM AUTHOR]