Question-focused Summarization by Decomposing Articles into Facts and Opinions and Retrieving Entities

This research focuses on utilizing natural language processing techniques to predict stock price fluctuations, with a specific interest in early detection of economic, political, social, and technological changes that can be leveraged for capturing market opportunities. The proposed approach includes the identification of salient facts and events from news articles, then use these facts to form tuples with entities which can be used to get summaries of market changes for particular entity and then finally combining all the summaries to form a final abstract summary of the whole article. The research aims to establish relationships between companies and entities through the analysis of Wikipedia data and articles from the Economist. Large Language Model GPT 3.5 is used for getting the summaries and also forming the final summary. The ultimate goal of this research is to develop a comprehensive system that can provide financial analysts and investors with more informed decision-making tools by enabling early detection of market trends and events.

Hybrid Recommendation System using Graph Neural Network and BERT Embeddings

Recommender systems have emerged as a crucial component of the modern web ecosystem. The effectiveness and accuracy of such systems are critical for providing users with personalized recommendations that meet their specific interests and needs. In this paper, we introduce a novel model that utilizes a Graph Neural Network (GNN) in conjunction with sentence transformer embeddings to predict anime recommendations for different users. Our model employs the task of link prediction to create a recommendation system that considers both the features of anime and user interactions with different anime. The hybridization of the GNN and transformer embeddings enables us to capture both inter-level and intra-level features of anime data.Our model not only recommends anime to users but also predicts the rating a specific user would give to an anime. We utilize the GraphSAGE network for model building and weighted root mean square error (RMSE) to evaluate the performance of the model. Our approach has the potential to significantly enhance the accuracy and effectiveness of anime recommendation systems and can be extended to other domains that require personalized recommendations.

Multi-BERT for Embeddings for Recommendation System

In this paper, we propose a novel approach for generating document embeddings using a combination of Sentence-BERT (SBERT) and RoBERTa, two state-of-the-art natural language processing models. Our approach treats sentences as tokens and generates embeddings for them, allowing the model to capture both intra-sentence and inter-sentence relations within a document. We evaluate our model on a book recommendation task and demonstrate its effectiveness in generating more semantically rich and accurate document embeddings. To assess the performance of our approach, we conducted experiments on a book recommendation task using the Goodreads dataset. We compared the document embeddings generated using our MULTI-BERT model to those generated using SBERT alone. We used precision as our evaluation metric to compare the quality of the generated embeddings. Our results showed that our model consistently outperformed SBERT in terms of the quality of the generated embeddings. Furthermore, we found that our model was able to capture more nuanced semantic relations within documents, leading to more accurate recommendations. Overall, our results demonstrate the effectiveness of our approach and suggest that it is a promising direction for improving the performance of recommendation systems

Hybrid Genetic Algorithm and Hill Climbing Optimization for the Neural Network

In this paper, we propose a hybrid model combining genetic algorithm and hill climbing algorithm for optimizing Convolutional Neural Networks (CNNs) on the CIFAR-100 dataset. The proposed model utilizes a population of chromosomes that represent the hyperparameters of the CNN model. The genetic algorithm is used for selecting and breeding the fittest chromosomes to generate new offspring. The hill climbing algorithm is then applied to the offspring to further optimize their hyperparameters. The mutation operation is introduced to diversify the population and to prevent the algorithm from getting stuck in local optima. The Genetic Algorithm is used for global search and exploration of the search space, while Hill Climbing is used for local optimization of promising solutions. The objective function is the accuracy of the trained neural network on the CIFAR-100 test set. The performance of the hybrid model is evaluated by comparing it with the standard genetic algorithm and hill-climbing algorithm. The experimental results demonstrate that the proposed hybrid model achieves better accuracy with fewer generations compared to the standard algorithms. Therefore, the proposed hybrid model can be a promising approach for optimizing CNN models on large datasets.