NLP Miniproject : Sentence to Sentence semantic similarity.

AIM — To create a mini project report on anyone chosen real world NLP application.

Introduction

Sentence-to-sentence semantic similarity is a fundamental concept in Natural Language Processing (NLP) that focuses on determining how similar or related two sentences are in terms of their meaning. This field plays a crucial role in various NLP applications, such as information retrieval, machine translation, text summarization, and question-answering systems. The goal of a sentence-to-sentence semantic similarity NLP project is to develop models and algorithms that can quantify the similarity between pairs of sentences based on their semantic content.

In today’s information-driven world, dealing with large volumes of text data is a common challenge. To extract meaningful insights, it is essential to have tools that can understand the underlying meaning of text beyond simple keyword matching. Sentence-to-sentence semantic similarity NLP projects aim to address this challenge by enabling computers to comprehend the context and semantics of text, making it easier to compare sentences and retrieve relevant information.

sentence-to-sentence semantic similarity is a crucial aspect of NLP with widespread applications. NLP projects in this domain aim to bridge the gap between human language understanding and machine processing, making it possible for computers to understand the subtle nuances of meaning in text, thereby enhancing the utility of NLP systems across various domains.

Objectives

The primary objectives of a sentence-to-sentence semantic similarity NLP project typically include:

Semantic Embeddings: Developing models that convert text into numerical representations (embeddings) that capture the semantic content of sentences. These embeddings should allow for the comparison of sentences based on their meaning rather than just their surface structure.

Similarity Measurement: Creating algorithms and metrics to quantify the similarity between sentence pairs using their embeddings. This involves finding methods to compare and contrast words, phrases, and structures within sentences.

Applications: Applying the developed models and techniques to various NLP applications. This can include information retrieval, where similar documents or sentences are ranked higher in search results, or machine translation, where translating sentences with similar meanings can improve translation quality.

Abstract

Natural Language Processing (NLP) has seen significant advancements in recent years, enabling computers to understand and process human language. One fundamental aspect of NLP is determining the semantic similarity between sentences, which holds immense practical value across various applications. This project focuses on the development of models and techniques to quantify the semantic similarity between pairs of sentences. Through the creation of semantic embeddings and similarity measurement algorithms, this endeavor aims to enhance information retrieval, machine translation, text summarization, and question-answering systems. Challenges in this project include data quality, semantic ambiguity, computational complexity, and multilingual considerations. Overcoming these challenges can lead to improved search engines that understand user queries better, more accurate machine translation systems, context-aware text summarization, and enhanced question-answering systems. By bridging the gap between human language understanding and machine processing, this project strives to empower NLP systems to interpret the intricate nuances of meaning in text, thereby unlocking new possibilities for NLP applications in the modern world.

Literature survey

1. Title — Universal Sentence Encoder published in 2018

Authors- Daniel Cer, Yinfei Yang, Sheng-yi Kong, et al.

Abstract-The “Universal Sentence Encoder” is an NLP model that creates fixed-length vectors to represent the meaning of sentences. It uses deep learning, including the Transformer architecture, and pre-trains on a large text dataset. This approach improves sentence-level NLP tasks like semantic similarity and sentiment analysis. The model is versatile and valuable for various NLP applications, making it a significant advancement in the field.

2. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding published in 2018

Authors- Jacob Devlin, Ming-Wei Chang, Kenton Lee, et al.

Abstract-The paper “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding” introduces the BERT model, which revolutionized Natural Language Processing (NLP). BERT uses deep bidirectional transformers to understand the contextual meaning of words in text. This pre-training approach significantly improves NLP tasks and has become a cornerstone in the field.

3. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks Published in: arXiv, 2019

Authors-Nils Reimers and Iryna Gurevych

Abstract-The paper “Sentence-BERT” introduces a novel approach to creating sentence embeddings for NLP tasks. It utilizes Siamese BERT-Networks to capture contextual sentence meaning and assess similarity. This method outperforms traditional embeddings, achieving state-of-the-art results in various NLP applications, enhancing the understanding of natural language text.

4. Learning Semantic Textual Similarity from Conversations Published in: arXiv, 2019

Authors-Kenton Lee, Ming-Wei Chang, Kenton Lee, et al.

Abstract-This research explores methods to learn semantic textual similarity from conversational data. It addresses the unique challenges of measuring sentence similarity in dialogues, making it highly relevant in the context of natural language conversations.

5. Siamese Recurrent Architectures for Learning Sentence Similarity

Authors-Jonas Mueller, Aditya Thyagarajan, and Bharath Sukhbaatar

Abstract-The “Sentence-BERT” paper introduces a method to create sentence embeddings using Siamese BERT-Networks. It improves sentence representations for NLP tasks, achieving state-of-the-art results, and enhancing the understanding of natural language text.

Output -

Here important libraries are imported and the dataset is loaded as well as visualized.

Here the vector returned is of tensorflow.python.framework.ops.EagerTensor which is not accepted in cosine similarity so it is converted into a numpy array.

Here cosine similarity is calculated which is required to calculate similarity score.

Similarity score for first five pair of text

Here pair of text with their similarity score and unique id is visualized

Here a submission.csv is downloaded which includes a unique id and similarity score.

Submission.csv file

Conclusion -

A sentence-to-sentence semantic similarity project aims to measure the likeness between two sentences in terms of their meaning or content. This endeavor begins with data collection, where a dataset of sentence pairs, along with similarity scores or labels, is gathered and preprocessed, involving steps like tokenization and embedding generation. The choice of an appropriate pre-trained language model or a custom-designed model is pivotal, as it serves as the foundation for the project. Fine-tuning may be necessary to enhance the model’s performance on the specific task. Evaluation metrics such as Pearson and Spearman correlation coefficients, Mean Squared Error, or others, help gauge the model’s effectiveness. Continuous refinement and hyperparameter tuning play a significant role in ensuring optimal results, as does interpreting the model’s predictions. Ethical considerations are vital to address potential bias in data and model outcomes, while thorough documentation is crucial to keep track of the project’s intricacies. As the project progresses, it may evolve, and scaling for large datasets and deployment in real-world applications can become part of the journey. Furthermore, monitoring, periodic retraining, and ethical vigilance remain ongoing concerns. This project is valuable for applications in natural language processing, aiding in information retrieval, recommendation systems, and sentiment analysis, facilitating users in finding relevant content and uncovering meaningful relationships between sentences.