Natural Language Processing with Keyphrase Extraction
Budget: $250 – $750 AUD
**Project Description:**
Title: AI-Based Keyphrase Extraction Using BERT and Unsupervised Embedding Approach
**Overview:**
This project focuses on developing an AI-based keyphrase extraction system for scientific documents. It aims to address the challenge of extracting keyphrases from longer documents by leveraging advanced NLP techniques. The project utilizes the S20RC dataset and incorporates tools like GROBID for document preprocessing.
**Objectives:**
1. Implement a keyphrase extraction system using BERT and unsupervised embedding approaches.
2. Utilize the S20RC dataset for training and evaluation.
3. Evaluate the system's performance using the F1 score metric.
4. Integrate GROBID for structured data extraction from PDF documents.
5. Develop a pipeline for document preprocessing, keyphrase extraction, and evaluation.
**Methodology:**
1. **Data Collection and Preprocessing:**
- Obtain the S20RC dataset containing scientific documents in PDF format.
- Use GROBID for extracting text content from PDFs and preprocessing.
2. **Model Development:**
- Fine-tune BERT models and explore unsupervised embedding techniques.
- Develop a hybrid model combining BERT and unsupervised embeddings for keyphrase extraction.
3. **Evaluation:**
- Split the dataset and evaluate the models using the F1 score metric.
4. **Results Analysis:**
- Analyze keyphrase extraction results and compare with manual annotations.
**Deliverables:**
1. Python code for the keyphrase extraction system.
2. Preprocessed S20RC dataset.
3. Trained BERT models and unsupervised embedding models.
4. Evaluation report with F1 score results.
5. Visualization of keyphrase extraction results.
**Conclusion:**
This project aims to develop an efficient keyphrase extraction system for scientific documents using AI techniques. By leveraging BERT and unsupervised embeddings, it seeks to achieve high-performance keyphrase extraction. The project contributes to advancements in NLP and information retrieval for scientific literature.
Title: AI-Based Keyphrase Extraction Using BERT and Unsupervised Embedding Approach
**Overview:**
This project focuses on developing an AI-based keyphrase extraction system for scientific documents. It aims to address the challenge of extracting keyphrases from longer documents by leveraging advanced NLP techniques. The project utilizes the S20RC dataset and incorporates tools like GROBID for document preprocessing.
**Objectives:**
1. Implement a keyphrase extraction system using BERT and unsupervised embedding approaches.
2. Utilize the S20RC dataset for training and evaluation.
3. Evaluate the system's performance using the F1 score metric.
4. Integrate GROBID for structured data extraction from PDF documents.
5. Develop a pipeline for document preprocessing, keyphrase extraction, and evaluation.
**Methodology:**
1. **Data Collection and Preprocessing:**
- Obtain the S20RC dataset containing scientific documents in PDF format.
- Use GROBID for extracting text content from PDFs and preprocessing.
2. **Model Development:**
- Fine-tune BERT models and explore unsupervised embedding techniques.
- Develop a hybrid model combining BERT and unsupervised embeddings for keyphrase extraction.
3. **Evaluation:**
- Split the dataset and evaluate the models using the F1 score metric.
4. **Results Analysis:**
- Analyze keyphrase extraction results and compare with manual annotations.
**Deliverables:**
1. Python code for the keyphrase extraction system.
2. Preprocessed S20RC dataset.
3. Trained BERT models and unsupervised embedding models.
4. Evaluation report with F1 score results.
5. Visualization of keyphrase extraction results.
**Conclusion:**
This project aims to develop an efficient keyphrase extraction system for scientific documents using AI techniques. By leveraging BERT and unsupervised embeddings, it seeks to achieve high-performance keyphrase extraction. The project contributes to advancements in NLP and information retrieval for scientific literature.