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Natural Language Processing

Natural Language Processing (NLP) is a discipline that deals with the interaction between computers and human language. Specifically, NLP seeks to program computers to process or analyze large amounts of natural language data (such as written or spoken text) in such a way that a coherent interpretation or production of the language is achieved.

Applications and use cases

Some of the main tasks of NLP include:

  1. Tokenization: splitting a text into words or other smaller units.
  2. Syntactic analysis: Determining the grammatical structure of a sentence.
  3. Lemmatization and stemming: Reducing words to their root or base.
  4. Named Entity Recognition (NER): Identify and categorize words in a text that represent proper nouns, such as names of people, organizations, or places.
  5. Sentiment analysis: Determine whether a text is positive, negative, or neutral.
  6. Automatic translation: Translate text from one language to another.
  7. Question answering: Generate answers to questions formulated in natural language.
  8. Natural language generation: Create coherent and contextually relevant texts.
  9. Automatic summarization: Create a concise summary of a longer text.


Creating an NLP model involves several steps, from data collection to model deployment:

  1. Problem definition: Before starting, it is essential to clearly define the problem you want to solve. Is it a sentiment analysis problem, machine translation, named entity recognition, or some other specific task?
  2. Data collection: Depending on the task, we will need a suitable dataset. We can use public datasets, create our own, or buy one.
  3. Data preprocessing: This is the task of preparing the information for model training. Specifically, in NLP we need to apply the following process:
    • Cleaning: Eliminating irrelevant data, correcting spelling errors, etc.
    • Tokenization: Split the text into words, phrases or other units.
    • Normalization: Converting all text to lowercase, lemmatization or stemming, etc.
    • Elimination of empty words (stopwords removal): Words such as "and", "or", "the", which do not contribute meaning in certain contexts.
    • Conversion to numbers: Neural networks, for example, work with numbers. Converting words to vectors.
    • Splitting the dataset: Separate the dataset into training and testing.
  4. Model building:
    • Architecture selection: Depending on the task, you can opt for traditional Machine Learning models, recurrent neural networks (RNN), convolutional neural networks (CNN) for text, transformers, etc.
    • Hyperparameter configuration: Define things like learning rate, batch size, number of layers, etc.
    • Model training: Use the training dataset to train the model while monitoring its performance on the validation set.
  5. Model evaluation: Once the model is trained, evaluate it using the appropriate metrics (accuracy, recall, F1-score, etc.) on the test set.
  6. Optimization: If the performance is not satisfactory, consider:
    • Adjust hyperparameters.
    • Change the model architecture.
    • Increase data.
    • Implement regularization techniques.
  7. Deployment: Once you are satisfied with the model's performance, you can deploy it to a server or application so that others can use it.

These steps provide a general structure, but each NLP project may have its own specificities and require adaptations. NLP model building is both an art and a science, and it often requires experimentation and iteration.