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PhD Projects in Commonsense Reasoning for NLP

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Python Projects in Commonsense Reasoning for NLP for Masters and PhD

    Project Background:
    The integration of common sense knowledge in NLP revolves around enhancing the capabilities of NLP systems to understand and generate human language in a more intelligent and context-aware manner. While NLP has made significant strides in language understanding, it often struggles with capturing the nuanced and implicit information humans take for granted in communication. The vast reservoir of general world knowledge humans possess is crucial for addressing this gap. It provides the context and background information needed to interpret language correctly, make inferences, resolve ambiguities, and generate coherent responses. Integrating commonsense knowledge into NLP systems has become a paramount research goal to improve applications across various domains, from chatbots and virtual assistants to healthcare, education, and content curation.

    Problem Statement

  • In this project, existing NLP models often struggle to comprehend the broader context of a conversation and apply common sense knowledge to interpret and respond effectively.
  • Excel in handling ambiguous language and making logical inferences based on context, crucial for resolving ambiguities and filling in information gaps.
  • Integrating common sense knowledge should be done cautiously to avoid propagating biases and stereotypes in the training data.
  • Curating a reliable and extensive repository of common sense knowledge is a substantial undertaking. Ensuring the accuracy and quality of this knowledge base is critical to the success of any integration effort.
  • Ensuring the scalability and updatability of integrated knowledge is a complex problem. As common sense knowledge evolves and expands, NLP systems must be capable of adapting and learning new information over time.

    • Aim and Objectives

  • Integrating common sense knowledge in NLP enhances the understanding, reasoning, and context-awareness of NLP systems, enabling them to provide more accurate, meaningful, and human-like responses in a wide range of applications.
  • Develop NLP models that perform basic common sense reasoning and answer questions based on general world knowledge.
  • Enable NLP systems to understand and adapt their responses to the specific context of conversations or documents.
  • Enhance the NLP models ability to make inferences and draw conclusions from available information, even when it is not explicitly stated.
  • Develop NLP models that can resolve linguistic ambiguities and understand subtle contextual cues.
  • Create a scalable and diverse repository of common sense knowledge that covers a wide range of topics and domains.
  • Ensure the accuracy and reliability of the common sense knowledge base through rigorous data collection and validation processes.
  • Enable NLP models to adapt to domains and scenarios, ensuring their utility in various real-world contexts.
  • Enhance user experiences by providing more informative, coherent, and contextually relevant interactions with NLP-powered systems.

    • Contributions to the Integration of Common Sense Knowledge in NLP

    1. By incorporating common sense knowledge, NLP systems can better understand the meaning of text and conversations, reducing the risk of misinterpretation or producing irrelevant responses.
    2. Integration efforts that focus on addressing biases and cultural sensitivity contribute to creating more responsible and ethical AI systems.
    3. Enhanced contextual awareness with common sense knowledge can adapt their responses to the specific context of a conversation or document, leading to more relevant and contextually appropriate interactions.
    4. By addressing biases and cultural sensitivity in knowledge integration, NLP systems can become more ethical and responsible in user interactions.
    5. Generate more coherent and contextually relevant text in applications like content generation, storytelling, and creative writing.
    6. Serve as educational aids, providing students with contextually relevant explanations and assisting their learning processes.

    Deep Learning Algorithms for the Integration of Common-Sense Knowledge in NLP

  • Recurrent Neural Networks (RNNs)
  • Long Short-Term Memory (LSTM) networks
  • Gated Recurrent Unit (GRU) networks
  • Graph Neural Networks
  • BERT (Bidirectional Encoder Representations from Transformers)
  • GPT (Generative Pre-trained Transformer)
  • RoBERTa
  • ERNIE
  • Knowledge Graph Embeddings
  • Reinforcement Learning for NLP with Knowledge Integration

    • Datasets for the Integration of Common Sense knowledge in NLP

  • ConceptNet
  • WordNet
  • ATOMIC
  • CommonsenseQA
  • SWAG
  • Choice of Plausible Alternatives (COPA)
  • ReCoRD
  • SocialIQA
  • Commonsense Knowledge Base
  • SQUAD-Commonsense
  • HellaSwag
  • WinoGrande

    • Performance Metrics

  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • BLEU
  • ROUGE
  • METEOR
  • Perplexity
  • Entity Recognition F1 Score
  • Semantic Role Labeling (SRL) F1 Score
  • Knowledge Graph Completion Metrics
  • Coherence and Fluency Assessment

    • 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

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