{"id":1852,"date":"2023-04-03T11:48:50","date_gmt":"2023-04-03T11:48:50","guid":{"rendered":"https:\/\/jenniferkwentoh.com\/?p=1852"},"modified":"2023-04-03T11:52:55","modified_gmt":"2023-04-03T11:52:55","slug":"mastering-nlp-create-powerful-language-models-with-python","status":"publish","type":"post","link":"https:\/\/jenniferkwentoh.com\/mastering-nlp-create-powerful-language-models-with-python\/","title":{"rendered":"Mastering NLP: Create Powerful Language Models with Python"},"content":{"rendered":"\n

Natural Language Processing (NLP)<\/a> has revolutionized the way we interact with computers, enabling them to understand and interpret natural human language in ways previously thought impossible. Whether it’s virtual assistants, language translation, or speech recognition, NLP is powering the next generation of intelligent applications.<\/p>\n\n\n\n

In this article, I will show you how to create powerful language models with Python, and take your NLP skills to the next level. By the end of this tutorial, you will have the knowledge and tools to create your own language models. So, let’s get started and master NLP together!<\/p>\n\n\n\n

Let’s start by exploring the concept of a language model and building one with python. <\/p>\n\n\n\n

What is a language Model?<\/p>\n\n\n\n

\n

A language model is a probability distribution over a sequence of words.<\/p>\n<\/blockquote>\n\n\n\n

In simpler words, it is a model that learns to predict the probability of a sequence of words.<\/span> <\/p>\n\n\n\n

Let’s play with some examples of “sequence of words”<\/p>\n\n\n\n

Which sequence of words is more accurate?<\/strong><\/p>\n\n\n\n

A. John likes to play <\/code><\/p>\n\n\n\n

B. Play John likes<\/code><\/p>\n\n\n\n

The first example follows a word order grammar rule (SVO) Subject - Verb - Object<\/code><\/p>\n\n\n\n

The second example doesn’t. <\/p>\n\n\n\n

Correct answer is A<\/strong><\/p>\n\n\n\n

Language Modeling is used in several Natural language processing projects like machine translation, auto-complete, auto-correct and speech recognition systems.<\/p>\n\n\n\n

Types of Language Models <\/h2>\n\n\n\n

Rule-based models <\/h3>\n\n\n\n

Rule-based models are language models that use a set of hand-crafted rules to generate and interpret natural language. These models can be effective for simple tasks but are often limited by their reliance on explicit rules.<\/p>\n\n\n\n

Statistical Language Models<\/h3>\n\n\n\n

Statistical language models use statistical techniques like probabilistic algorithms and linguistic rules to learn to predict the probability of a sequence of words.<\/p>\n\n\n\n

Examples are N-grams, Hidden Markov Models (HMM)<\/p>\n\n\n\n

Neural Language Models <\/h3>\n\n\n\n

Neural language models use different neural networks and deep learning algorithms to analyze and interpret natural language. These models can achieve state-of-the-art results.<\/p>\n\n\n\n

Neural language models are often more complex than statistical models and they require large amounts of training data.<\/p>\n\n\n\n

Examples include; Recurrent Neural Networks (RNNs). RNNs are good at modeling long-term dependencies between words in a sentence.<\/p>\n\n\n\n

Transformer Models: Transformer models use self-attention mechanisms to process sequential data. <\/p>\n\n\n\n

Examples of transformer models are BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pretrained Transformer).<\/p>\n\n\n\n

Hybrid Models<\/h3>\n\n\n\n

Hybrid language models combine multiple approaches, such as rule-based, statistical, and neural models.<\/p>\n\n\n\n

Knowledge-based Models<\/h3>\n\n\n\n

Knowledge-based models use structured data, such as ontologies and semantic networks, to analyze and generate natural language. These models are effective for tasks that require a deep understanding of language semantics.<\/p>\n\n\n\n

Let’s jump right into it with a few examples using Python.<\/h2>\n\n\n\n

Unlocking the Power of Language: Building an N-Gram Language Model with Python<\/h2>\n\n\n\n

What are N-grams?<\/h3>\n\n\n\n

N-grams refer to a series or sequence of N consecutive tokens or words.<\/strong><\/p>\n\n\n\n

There are several types of N-grams based on the number of tokens or words in the sequence:<\/p>\n\n\n\n

    \n
  1. Unigrams: These are N-grams with a single token or word.<\/li>\n\n\n\n
  2. Bigrams: These are N-grams with two tokens or words.<\/li>\n\n\n\n
  3. Trigrams: These are N-grams with three tokens or words.<\/li>\n\n\n\n
  4. 4-grams (Quadgrams): These are N-grams with four tokens or words.<\/li>\n\n\n\n
  5. 5-grams (Pentagrams): These are N-grams with five tokens or words.<\/li>\n\n\n\n
  6. N-grams with higher values of N, such as 6-grams (Hexagrams), 7-grams (Heptagrams), and so on.<\/li>\n<\/ol>\n\n\n\n

    The choice of N in N-grams depends on the application and the complexity of the language. For example, bigrams and trigrams are commonly used in language modeling tasks, while higher-order N-grams may be used for more complex language analysis.<\/p>\n\n\n\n

    For an example, consider the following sentence:<\/p>\n\n\n\n

    \"The big brown fox jumped over the fence\"<\/code><\/p>\n\n\n\n

      \n
    1. Unigrams would be: \"The\", \"big\", \"brown\", \"fox\", \"jumped\", \"over\", \"the\", \"fence\"<\/code><\/li>\n\n\n\n
    2. Bigram: \"The big\", \"big brown\", \"brown fox\", \"fox jumped\", \"jumped over\", \"over the\", \"the fence\"<\/code><\/li>\n\n\n\n
    3. Trigram: \"The big brown\", \"big brown fox\", \"brown fox jumped\", \"fox jumped over\", \"jumped over the\", \"over the fence\"<\/code><\/li>\n\n\n\n
    4. 4-gram (Quadgram): \"The big brown fox\", \"big brown fox jumped\", \"brown fox jumped over\", \"fox jumped over the\", \"jumped over the fence\"<\/code><\/li>\n\n\n\n
    5. 5-gram (Pentagram): \"The big brown fox jumped\", \"big brown fox jumped over\", \"brown fox jumped over the\", \"fox jumped over the fence\"<\/code><\/li>\n\n\n\n
    6. 6-gram (Hexagram): \"The big brown fox jumped over\", \"big brown fox jumped over the\", \"brown fox jumped over the fence\"<\/code><\/li>\n<\/ol>\n\n\n\n

      Example: Predict the next word<\/h4>\n\n\n\n
      \"language<\/figure>\n\n\n\n

      To predict the next word in a sentence, we can use a trigram model (N=3) <\/p>\n\n\n\n

      This model evaluates the likelihood of every potential next word based on the two previous words. This is achieved by calculating the frequency of each trigram in a training corpus and subsequently estimating the probability of each trigram.<\/p>\n\n\n\n

      Now that we understand what N-grams are, let’s move on to implementing N-gram models with Python.<\/p>\n\n\n\n

      Install NLTK using pip<\/strong><\/p>\n\n\n\n

      pip install nltk<\/code><\/pre>\n\n\n\n
      We will be using the Reuters corpus, which is a collection of news documents.<\/p>\n\n\n\n
      download the necessary data:<\/strong><\/p>\n\n\n\n
      import nltk\nnltk.download('punkt')\nnltk.download('reuters')<\/code><\/pre>\n\n\n\n
      \nfrom nltk.corpus import reuters\nfrom nltk import ngrams, FreqDist\n\n# Load the Reuters corpus\ncorpus = reuters.words()\n\n# Tokenize the corpus into trigrams\nn = 3\ntrigrams = ngrams(corpus, n)\n\n# Count the frequency of each trigram\nfdist = FreqDist(trigrams)<\/code><\/pre>\n\n\n\n
      <\/p>\n\n\n\n
      To begin, we load the Reuters corpus using the reuters.words()<\/code> function, which returns a list of words in the corpus.<\/p>\n\n\n\n
      Afterward, we utilize the ngrams()<\/code> function to create trigrams by tokenizing the corpus, with the function accepting two arguments: the corpus itself and N (in this case, 3 for trigrams).<\/p>\n\n\n\n
      we count the frequency of each trigram using the FreqDist() <\/code>function.<\/p>\n\n\n\n
      With the frequency distribution of the trigrams, we can calculate probabilities and make predictions.<\/p>\n\n\n\n
      # Define the context of the sentence we want to predict\ncontext = ('we', 'are')\n\n# Get the list of possible next words and their frequencies\nnext_words = [x[0][2] for x in fdist.most_common() if x[0][:2] == context]\n\n# Print the next word\nprint(next_words, end=' ')\n<\/code><\/pre>\n\n\n\n