Project Background:
Key phrase generation and extraction focuses on developing algorithms and techniques to identify and extract key phrases from textual data automatically. Key phrases are essential for summarizing the main topics and concepts within a document, facilitating information retrieval, categorization, and analysis. Traditional approaches to keyphrase generation often rely on statistical methods or rule-based heuristics and struggle to capture the semantics and context of natural language. In contrast, modern deep learning techniques, such as RNNs, transformer-based models, and GNNs, offer the potential to learn complex patterns and relationships within textual data, enabling more accurate and contextually relevant keyphrase extraction. By leveraging these advanced algorithms, this project aims to improve the efficiency and effectiveness of keyphrase generation tasks across various domains, including academic literature, news articles, and online content. Additionally, the project seeks to address challenges such as domain-specific terminology, document length, and multi-document summarization, enhancing the capabilities of automated text analysis and information retrieval systems.
Problem Statement
- Existing methods for keyphrase generation often struggle to capture the semantic meaning and context of the text, leading to inaccurate or irrelevant keyphrases.
- Natural language is inherently ambiguous, making identifying the most salient key phrases challenging, especially in documents with multiple topics or complex content.
- Keyphrases can vary significantly in length, from single words to longer phrases or sentences, posing a challenge for standard extraction algorithms.
- Extracting keyphrases from multiple documents or larger text corpora requires algorithms capable of aggregating information and identifying overarching themes or concepts.
- Scalability is a concern when processing large volumes of text data, requiring efficient algorithms that can handle the computational demands of keyphrase extraction.
Aim and Objectives
- Develop efficient algorithms for automated keyphrase generation and extraction from textual data.
- Enhance semantic understanding to identify contextually relevant keyphrases accurately.
- Address variability in keyphrase length and domain-specific terminology for improved extraction accuracy.
- Enable multi-document summarization by aggregating information from multiple sources to generate comprehensive keyphrases.
- Develop scalable algorithms to efficiently handle large volumes of text data while maintaining extraction accuracy.
Contributions to Keyphrase Generation and Extraction
- Enhancing algorithms to accurately identify keyphrases that capture the semantic meaning and context of the text.
- Developing methods to address variability in keyphrase length and domain-specific terminology, leading to more accurate extractions.
- Enabling algorithms to aggregate information from multiple documents for comprehensive keyphrase generation, improving summarization capabilities.
- Introducing scalable algorithms capable of efficiently processing large volumes of text data while maintaining high extraction accuracy, enhancing usability in real-world applications.
Deep Learning Algorithms for Keyphrase Generation and Extraction
- Recurrent Neural Networks (RNNs)
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Long Short-Term Memory (LSTM) networks
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Transformer-based models (BERT, GPT)
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Graph Neural Networks (GNNs)
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Convolutional Neural Networks (CNNs)
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Attention Mechanisms
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Sequence-to-Sequence models
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Pointer Networks
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Variational Autoencoders (VAEs)
Datasets for Keyphrase Generation and Extraction
- SemEval-2010 dataset
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KP20k dataset
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Inspec dataset
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DUC-2001 dataset
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OpenKP dataset
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KPTimes dataset
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StackExchange dataset
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Wikipedia dataset
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PubMed dataset
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arXiv dataset
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1.Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2.Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
