Introduction to Natural Language Processing for Research: Key Points

Introduction to Natural Language Processing

Image datasets can be found online or created uniquely for your research question.
Images consist of pixels arranged in a particular order.
Image data is usually preprocessed before use in a CNN for efficiency, consistency, and robustness.
Input data generally consists of three sets: a training set used to fit model parameters; a validation set used to evaluate the model fit on training data; and a test set used to evaluate the final model performance.

Text preprocessing is essential for cleaning and standardizing text data.
Techniques like sentence segmentation, tokenization, stemming, and lemmatization are fundamental to text preprocessing.
Removing stop-words helps in focusing on the important words in text analysis.
Tokenization splits sentences into tokens, which are the basic units for further processing.

Named Entity Recognition (NER) is crucial for identifying and categorizing key information in text, such as names of people, organizations, and locations.
Topic Modeling helps uncover the underlying thematic structure in a large corpus of text, which is beneficial for summarizing and understanding large datasets.
Text Summarization provides a concise version of a longer text, highlighting the main points, which is essential for quick comprehension of extensive research material.

Tokenization is crucial for converting text into a format usable by machine learning models.
BoW and TF-IDF are fundamental techniques for feature extraction in NLP.
Word2Vec and GloVE generate embeddings that encapsulate word meanings based on context and co-occurrence, respectively.
Understanding these concepts is essential for building effective NLP models that can interpret and process human language.

Transformers revolutionized NLP by processing words in parallel through an attention mechanism, capturing context more effectively than sequential models
The summation and activation function within a neuron transform inputs through weighted sums and biases, followed by an activation function to produce an output.
Transformers consist of encoders, decoders, positional encoding, input/output embedding, and softmax output, working together to process and generate data.
Transformers are not limited to NLP and can be applied to other AI applications due to their ability to handle complex data patterns.
Sentiment analysis and text summarization are practical applications of transformers in NLP, enabling the analysis of emotional tone and the creation of concise summaries from large texts.

LLMs are based on the transformer architecture.
BERT and GPT have distinct approaches to processing language.
Open source LLMs provide transparency and customization for research applications.
Benchmarking with HELM offers a holistic view of model performance.

Domain-specific LLMs are essential for tasks that require specialized knowledge.
Prompt engineering, RAG, fine-tuning, and training from scratch are viable approaches to create DSLs.
A mixed prompting-RAG approach is often preferred for its balance between performance and resource efficiency.
Training from scratch offers the highest quality output but requires significant resources.

Various NLP techniques from preprocessing to advanced LLMs are reviewed.
NLPs’ transformative potential provides real-world applications in diverse fields.
Few-shot learning can enhance the performance of LLMs for specific fields of research.
Valuable resources are highlighted for continued learning and exploration in the field of NLP.