Understanding Natural Language Processing (NLP)

Language is a fundamental human ability, enabling us to transfer knowledge and communicate effectively. From spoken words to written text, languages vary greatly in structure and complexity. While AI has made significant strides in image processing, interacting with computers primarily relies on language. We use language to search the internet, control devices, and even get help with tasks like translation. This leads us to the fascinating field of Natural Language Processing (NLP).

What is Natural Language Processing?

Natural Language Processing (NLP) focuses on two core areas:

• Natural Language Understanding (NLU): This involves extracting meaning from text, enabling AI to perform tasks like filtering spam, understanding search queries, and guiding selfdriving cars.
• Natural Language Generation (NLG): This focuses on generating humanlike text, used in applications such as translation, summarization, and chatbots.

The central challenge in both areas is enabling AI to understand the meaning of words, which is complicated by ambiguity and context dependence.

The Challenge of Meaning

Words, on their own, lack inherent meaning. We assign meaning to symbols, and this meaning can change depending on context. For example, the word "bank" can refer to a financial institution or the edge of a river.

We learn the meaning of words through association and context. A child learns that a "cat" is a furry animal that purrs. When developing NLP systems, it's crucial to consider how AI will learn word meanings and handle potential ambiguities.

Approaches to Understanding Word Meaning

Several techniques help AI understand word relationships:

• Morphology: Analyzing word structure, like the root word "swim" and its variations (swimming, swimmer). However, this approach doesn't work for all words.
• Distributional Semantics: Determining word meaning based on the words that frequently appear alongside it in sentences. This approach leverages insights from linguistics. As linguist John Firth said: "You shall know a word by the company it keeps."

Count Vectors

A simple technique to implement distributional semantics is using count vectors. A count vector represents the frequency of a word's cooccurrence with other common words in a text.

By comparing count vectors, we can infer the similarity of word meanings. For example, "cat" and "Felidae" (the scientific family of cats) are likely to have similar count vectors, indicating similar meanings.

However, a limitation of count vectors is the massive amount of data required to store word cooccurrences.

EncoderDecoder Models

To address the limitations of count vectors, we can use encoderdecoder models, which learn compact representations of words while preserving semantic relationships. These models are similar to those used in unsupervised learning for image analysis.

The encoder processes input text and creates an internal representation, while the decoder uses this representation to generate language or perform a specific task.

Language Modeling: A FillintheBlank Example

Consider the sentence: "I'm kinda hungry, I think I'd like some chocolate _____."

A language model would predict the most likely words to fill the blank, such as "cake" or "milk." The encoder focuses on the word "chocolate" to guide the decoder in selecting a suitable word from a cluster of "chocolate food words."

Neural Networks for Language Modeling

To enable computers to perform language modeling, we can use neural networks. We train the network on a large corpus of text, playing a fillintheblank game for each word in every sentence.

Recurrent Neural Networks (RNNs)

A suitable type of neural network for language modeling is a Recurrent Neural Network (RNN). RNNs have a loop that allows them to reuse a single hidden layer, updated sequentially as the model reads one word at a time. This enables the model to capture contextual information and grammatical properties related to meaning.

Word Embeddings

We can't directly feed words into a neural network. Therefore, we assign each word a random vector representation. The model then learns the optimal representation for each word through unsupervised learning.

The encoder takes in these word representations and combines them into a shared representation for the whole sentence. The decoder then uses this representation to predict the next word in the sentence.

During training, the model adjusts the weights in the encoder RNN and the decoder prediction layer. Importantly, the model modifies the initial random word vectors, making vectors for similar words more similar. This allows us to visualize word relationships by plotting word vectors.

The Potential of NLP

Predicting the next word is just the beginning for NLP. By modifying the model, we can create systems for translation, question answering (like Siri or Alexa), and controlling robots.

However, it's important to note that the word representations learned for one task may not be suitable for another. Acquiring, encoding, and using written or spoken knowledge to help people is a huge and exciting task, because we use language for so many things! Every time you type or talk to a computer, phone or other gadget, NLP is there.