Project Title:Named Entity Recognition (NER)

Objective:
To build a system that can automatically identify and classify named entities in text into predefined categories such as person names, organizations, locations, dates, quantities, and more.

Project Overview:

Named Entity Recognition (NER) is a core task in Natural Language Processing (NLP), where the goal is to locate and classify entities in unstructured text. This project aims to develop a model that can read raw text and detect important information like names of people, companies, cities, and other key entities. NER is widely used in information extraction, chatbots, search engines, question answering systems, and document summarization.

Key Steps in the Project:

Data Collection:

Use standard datasets such as:

CoNLL-2003

SpaCy’s built-in datasets

OntoNotes

These datasets come with annotated labels for named entities.

Data Preprocessing:

Tokenize the text (split into words).

Lowercase, remove punctuation or special characters if needed.

Align labels with tokens (for models like BERT that use subword tokenization).

Handle BIO tagging format (e.g., B-PER, I-PER, O for Outside any entity).

Model Selection:

Rule-based methods (using SpaCy or Regex).

Machine Learning models:

CRF (Conditional Random Fields)

HMM (Hidden Markov Models)

Deep Learning models:

BiLSTM + CRF

Transformers (e.g., BERT, RoBERTa) fine-tuned for NER.

Model Training:

Train on labeled datasets.

Use word embeddings (Word2Vec, GloVe) or contextual embeddings (BERT).

Fine-tune transformer models for best results on entity classification.

Model Evaluation:

Metrics: Precision, Recall, F1-score (evaluated per entity type).

Confusion matrix to understand misclassifications.

Deployment:

Integrate the trained NER model into an application like:

A search engine to highlight key entities.

A chatbot to identify user names, locations, etc.

A resume parser or legal document analyzer.

Tools & Technologies:

Programming Language: Python

Libraries/Frameworks:

SpaCy, NLTK, Hugging Face Transformers

Scikit-learn, Flair, TensorFlow, PyTorch

Deployment: Flask/Django (for web apps), Streamlit (for demos)

Conclusion:

NER is a practical and impactful NLP project that teaches students how to handle text data, perform sequence labeling, and build models that extract structured information from unstructured sources. It strengthens understanding of both traditional ML and modern deep learning (especially transformers), and is highly relevant in domains like healthcare, finance, legal tech, and digital assistants.