Named Entity Recognition (NER) is the task of detecting and categorizing the entities in a given text. It is an important task in Natural Language Processing (NLP) and forms the basis of many NLP systems. Previous work on NER that makes use of statistical models can be categorized into two main categories: feature-based and embedding-based. Earlier work on NER made frequent use of manually crafted features. In order to use manually crafted features, we either automatically annotate the dataset for the given features using third-party software or manually annotate the dataset, and both require additional work. Recent work makes use of BiLSTM based neural networks and represents words with embeddings. This relieves systems from re-lying on manually created feature sets. In this work, we started by analyzing the performance of the feature-based systems. In this phase, we reimplemented a previous work and improved the performance by making use of dependency parsing features additional. Following these results, we implemented a novel method that makes use of both dependency parsing features and embeddings. We propose a novel BiLSTM CRF-based neural model that makes use of the dependency parsing feature to improve the named entity recognition performance in a unique way. Our model jointly learns both dependency parsing and named entity recognition using separate datasets for each task. The model does not require the named entity recognition dataset to be annotated for the dependency parsing task. Our results show that performance increases when we use a joint learning model instead of annotating the named entity recognition dataset automatically. We also show that the dependency parser, which we have implemented to improve the named entity recognition, has comparable results with the previous work.