NameEntityRecognition

#Project Title: Named Entity Recognition with NER Dataset Description: Our project focuses on implementing Named Entity Recognition (NER) using a specialized NER dataset. Named Entity Recognition is a natural language processing (NLP) task that involves identifying and categorizing entities (such as names of persons, organizations, locations, etc.) within a body of text. Key Features: NER Dataset Utilization: We utilize a dedicated NER dataset tailored for training and evaluating our NER model. This dataset is annotated with named entities, providing valuable labeled data for training our model. Model Architecture: Our implementation employs state-of-the-art deep learning architectures for NER, such as Bidirectional LSTMs (Long Short-Term Memory networks) with CRF (Conditional Random Fields) layers. This architecture has demonstrated superior performance in various NLP tasks, including NER. Preprocessing Techniques: We incorporate essential preprocessing techniques to prepare the input text data for NER. This includes tokenization, word embedding, and possibly other text normalization methods tailored to optimize the performance of our NER model.