Natural Language Processing (NLP) language modeling is in vogue these days. They help applications execute multiple tasks like speech recognition, speech-to-text conversion, machine translation, sentiment analysis, and much more. These models form the core of NLP.
As companies continue to invest in artificial intelligence (AI) and related technologies, NLP language models will continue to grow in significance. Research suggests that the market is expected to grow from around $20 billion in 2021 to $26 billion by the end of 2022.
This article explains language models in NLP, how they work, their types, and examples.
What is Language Modeling in NLP?
Have you seen the Smart Reply and Smart Compose features in Gmail? They help you quickly reply to emails or complete the text you are typing. This is how language models are used in NLP, a branch of AI.
Language modeling refers to different probabilistic and statistical methods used to determine the chances of a given series of words occurring in a text. These models analyze text and provide predictions as to which words might come next.
NLP applications use these models as a basis for word predictions, especially ones that produce text as output. Question-answering and machine translation applications are common examples.
How does it work?
NLP models analyze text to give the word probability. This interpretation first entails feeding the text data to an algorithm that specifies rules for context in natural language. The model then uses these rules and applies them to language tasks to create new sentences or give accurate predictions.
In other words, the model imbibes a language’s fundamental characteristics and features and uses them to understand new sentences or phrases. The same is used to make word/phrase predictions.
Various probabilistic approaches are employed in training a language model. But the approach used is determined by the purpose of the language model. The difference between the approaches generally revolves around the amount of text analyzed and the type of math used.
Let’s look at the types of language models.
Types of Language Models in NLP
There are essentially two types of language models.
1. Statistical Language Models
Statistical language models involve creating probabilistic models able to identify (by means of prediction) the next word in a phrase or sequence based on the words preceding it.
Quite a few statistical models exist today. Let’s look at some of these briefly.
N-Gram – It is an easy-to-use approach in language modeling. N-Gram creates a probability distribution for a sequence of ‘n.’ The ‘n’ defines the gram’s size or a series of words being assigned a probability and can be any number. So, suppose if n=3, a gram may look like “Are you there.” Think of ‘n’ as the amount of context the model has to consider. There are various types of N-grams like unigrams, bigrams, trigrams, etc.
Unigram – It is often considered the most uncomplicated kind of language model. It does not assume any conditioning context while executing calculations. Instead, it analyzes each phrase, term, or word independently. Unigrams are commonly used to perform language processing tasks like information retrieval. It serves as the basis for the query likelihood model, which is more specific and uses information retrieval to evaluate a cohort of documents and match the most appropriate one to a particular query.
Bidirectional – Whereas N-gram models analyze text backward only, bidirectional models analyze text both ways – forward and backward. It uses all the words in a text body to predict any word or sentence in a body of text. Thus, bidirectional examination of text increases accuracy manifold. It is often employed in speech generation applications and machine learning. Google technology also uses a bidirectional model for search queries.
Exponential – It uses a combination of feature functions and n-grams using an equation to evaluate text. The exponential model lays down the features and parameters of desired results. It is based on the principle of entropy, which says that probability distribution with the highest entropy is the best option. These models maximize cross-entropy, thus limiting the number of assumptions one can make. Hence, it offers high accuracy.
Continuous Space – It shows words as a non-linear combination of weights in a neural network. The weight-assigning process is called word embedding. It is a very useful model where large datasets with unique words are involved. Too many words (rarely used) often cause problems for linear models like n-gram.
2. Neural Language Models
These models are considered an advanced way of executing NLP tasks based on neural networks. Neural language models come without the shortcomings of classical models like n-grams. They are used in complex operations like machine translation and speech recognition.
Language is an ever-evolving phenomenon. New words enter the lexicon, and old words lose favor with people regularly. Thus, a more complex model is better suited to perform NLP tasks. Moreover, language models must be able to identify dependencies, for instance, by knowing how words are derived from various languages.
Examples of Language Models in NLP
Here are some common examples and use cases of language models in natural language processing.
- Speech recognition – The best examples of speech recognition software are voice assistants like Siri and Alexa. They can process human audio speech.
- Machine translation – It entails translating one language into another. Microsoft Translator and Google Translate are two leading examples of how NLP models aid in language translation.
- Text suggestions – As stated earlier, Gmail uses text suggestions through its Smart Compose feature using NLP models. These suggestions help you write text in emails and long documents.
- Sentiment analysis – It helps identify the sentiment of a phrase or the opinion and attitude represented by a body of text. Many companies now employ sentiment analysis to gauge customer and employee feedback. It enables them to tailor their products and services accordingly. Google’s NLP tool, Bidirectional Encoder Representations from Transformers (BERT), is an example of a sentiment analysis tool.
- Parsing – It involves the examination of words or sentences that follow grammar and syntax rules. Spell-check applications and the autocorrect feature on your smartphone use parsing and language modeling.
- Optical character recognition – It is the mechanical or electronic conversion of images of text into machine-encoded text. The image can be a document photo, a scanned document, or any image with text in it.
Language modeling is fast becoming a critical part of our digital lives. Language models have become deeply embedded in our lives, from using Alexa to play our Spotify playlist to using autocorrect on our smartphones for better communication.
The power of language models, NLP, and artificial intelligence will only grow in the coming years.
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