Project Background:
The Semantic Similarity using deep learning centers on addressing the complex task of measuring the similarity between pieces of text or data, focusing on the underlying meaning. Traditional methods for assessing semantic similarity often fail to capture relationships within language. Deep Learning in neural network architecture has emerged as a powerful tool for learning intricate patterns and representations in data. In this context, this project aims to leverage Siamese Networks, Recurrent Neural Networks (RNNs), or Transformer models to capture the semantic relationships between textual elements. It also seeks to enhance the accuracy and contextual understanding of similarity measures and enable applications ranging from natural language processing tasks, such as information retrieval and question answering, to content recommendation systems.
Problem Statement:
- The problem statement revolves around the limitations of traditional methods in accurately capturing and quantifying the semantic relationships between pieces of text.
- Existing approaches often struggle to discern subtle contextual nuances and semantic intricacies, leading to suboptimal performance in tasks such as information retrieval, question answering, and content recommendation.
- The challenge lies in developing a system that can effectively and efficiently learn and represent the underlying semantics of diverse textual data.
- However, the challenge persists in designing and implementing architectures that robustly handle varying sentence structures, linguistic styles, and semantic intricacies.
- The project aims to address these limitations by employing advanced deep learning techniques to enhance the precision and depth of semantic similarity measurements to more accurate and aware systems in natural language understanding and information processing.
Aim and Objectives:
- The aim is to enhance semantic similarity measurements by applying deep learning techniques to improve the accuracy and contextual understanding of relationships within textual data.
- Develop and implement deep learning models for capturing semantic representations in textual data.
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Explore and integrate advanced natural language processing techniques to preprocess and enhance the quality of input data.
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Evaluate and compare the performance of different models in measuring semantic similarity across diverse domains.
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Address challenges related to varying sentence structures, linguistic styles, and contextual nuances within the data.
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Provide an efficient framework for semantic similarity tasks, facilitating seamless integration into applications like information retrieval, question answering, and content recommendation.
Contributions to Semantic Similarity using Deep Learning:
- Deep Learning models contribute by learning rich and hierarchical representations of textual data, capturing intricate semantic relationships that may be challenging for traditional methods.
- The application techniques lead to more accurate and contextually aware semantic similarity measurements, allowing for better differentiation and understanding of textual content.
- Excel in handling diverse linguistic styles, accommodating variations in sentence structures, word usage, and contextual intricacies by enhancing the robustness of semantic similarity assessments.
- It provides scalable solutions, allowing efficient processing and analysis of large datasets, which is crucial for applications involving extensive textual content, such as information retrieval systems.
- The flexibility of deep learning models enables them to adapt to various domains and applications, making them suitable for semantic similarity tasks across different fields, from healthcare to finance.
- It has also impacted the broader field of natural language understanding by advancing in capturing semantic meaning, facilitating better comprehension of textual data.
- The development of open-source implementations and benchmark datasets contributes to the research community, fostering collaboration and enabling the comparison of various models in the field of semantic similarity.
Deep Learning Algorithms for Semantic Similarity:
- Siamese Networks
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Convolutional Neural Networks (CNN)
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Recurrent Neural Networks (RNN)
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Long Short-Term Memory (LSTM) Networks
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Gated Recurrent Units (GRU)
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Recursive Neural Networks (RvNN)
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Skip-Thought Vectors
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InferSent
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Universal Sentence Encoder (USE)
Datasets for Semantic Similarity using Deep Learning:
- STS Benchmark (Semantic Textual Similarity)
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Quora Question Pairs
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SNLI (Stanford Natural Language Inference)
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SICK (Sentences Involving Compositional Knowledge)
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MSRP (Microsoft Research Paraphrase Corpus)
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PubMed Semantic Textual Similarity Dataset
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STS SemEval (Semantic Evaluation) datasets
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STS Twitter datasets
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STS Wikipedia datasets
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STS Arabic and Cross-Lingual datasets
Performance Metrics for Semantic Similarity using Deep Learning:
- Pearson Correlation Coefficient
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Spearman Rank Correlation Coefficient
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Mean Squared Error (MSE)
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Root Mean Squared Error (RMSE)
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Mean Absolute Error (MAE)
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Precision
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Recall
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F1 Score
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Area Under the Receiver Operating Characteristic curve (AUC-ROC)
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Area Under the Precision-Recall curve (AUC-PR)
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Normalized Discounted Cumulative Gain (NDCG)
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Kendalls Tau
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Concordance Correlation Coefficient (CCC)
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Word Movers Distance (WMD)
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Earth Movers Distance (EMD)
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Jaccard Index
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Cosine Similarity
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BLEU Score
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ROUGE Score
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
