Natural Language Processing (NLP) is a field of study that focuses on the interaction between human language and computers. It involves developing algorithms and models that can understand, interpret, and generate natural language, which is often ambiguous and context-dependent.
NLTK
NLTK (Natural Language Toolkit) is a popular Python library for NLP that provides tools and resources for tasks such as tokenization, stemming, lemmatization, part-of-speech tagging, and named entity recognition. It also includes a wide range of corpora and datasets for training and evaluation of NLP models. NLTK can be used for both research and production-level applications.
Neural Networks
Neural Networks are a type of machine learning model that are loosely inspired by the structure and function of the human brain. They consist of interconnected layers of nodes (neurons) that learn to perform complex tasks through a process of training on labeled data. Neural Networks have been successfully applied to many NLP tasks such as language modeling, machine translation, sentiment analysis, and text classification.
In summary of above 3 topics, NLP is a field of study that deals with the interaction between human language and computers. NLTK is a Python library that provides tools and resources for NLP tasks, and Neural Networks are a type of machine learning model that can be used to solve NLP problems.
Text Pre-processing and its order
The correct order of text pre-processing steps may vary depending on the specific task and the requirements of the machine learning model. However, here is a general order of text pre-processing steps that is commonly followed:
- Lowercasing: Convert all the text to lowercase, as it can help with normalization and prevent duplication of words.
- Tokenization: Split the text into individual words or tokens, so that the model can process each word individually.
- Stop words removal: Remove common words that are unlikely to have any significance in the context of the task. Examples include words like “the”, “and”, “a”, etc.
- Stemming/Lemmatization: Reduce words to their root form, so that similar words are treated the same way. Stemming reduces words to a common stem or base form, whereas lemmatization reduces words to their base form using a vocabulary and morphological analysis of the word.
- Spell Checking and Correction: Correct common spelling mistakes to ensure the accuracy of the model.
- Feature Engineering: Convert the pre-processed text into numerical features that the machine learning model can understand. This may involve techniques such as bag of words, n-grams, and TF-IDF.
- Encoding: Encode the text features into a format that the machine learning model can handle, such as one-hot encoding or word embeddings.
It’s worth noting that not all of these pre-processing steps may be necessary or relevant for every task. Additionally, some steps may be combined or reordered depending on the specific requirements of the task or model.