An Overview of Deep Learning applied to Natural Language Processing
Deep learning combined with natural language processing empowers AI to comprehend and create human language. Read on to learn how and where this is used.
Natural language processing (NLP) is a field of study in computer science that applies artificial intelligence (AI) to linguistics. The aim of NLP is to understand language as a human would, both in written and spoken forms, as well as to be able to reproduce language in a natural manner. Understanding human language means not only comprehending words and their definitions, but also understanding context, emotions, intent, and the various other subtextual information conveyed through language. Read on to learn all about NLP and how it relates to deep learning.
What is Natural Language Processing?
AI and machine learning have significantly changed the way we interact with the world. Though many people may not realize it, NLP has become an everyday part of many peoples’ lives. For example, Gmail uses deep learning and NLP to power its ‘Smart Compose’ system. Smart compose helps users by providing predictive suggestions for what to write based on context. In addition, ‘smart assistants’ such as Siri and Alexa use NLP to understand and interpret spoken commands. These NLP use cases have all filtered through to the general public, often without many people realizing what is powering the technology behind them.
So what does NLP involve? NLP includes computational linguistics, computer study, statistical modeling, and deep learning. It understands the meaning of human language through analyzing a wide range of aspects, such as semantics, syntax, and morphology. With the help of NLP, machines are able to conduct semantic and emotion analysis and perform speech recognition tasks and text summarization. It can also be used in translation services, to provide better translations that convey not only the literal translation, but also maintain meaning, subtext, and emotion as much as possible.
The history of NLP
Early NLP methods generally involved hard-coded sets of rules combined with dictionary look-ups. However, since the late 1980s, what is known as the ‘statistical revolution’ led to methods of statistical inference that allowed machine learning systems to automatically learn based on sets of rules. An example of this would be the use of elaborate ‘decision trees’, which essentially were a very big series of ‘if-else’ statements that could be applied to make decisions about meaning in the text. However, these early methods still largely relied on a lot of manual involvement when developing rules that could be followed.
In more recent years, more advanced AI techniques such as deep learning have been applied to NLP. Deep learning systems have a large advantage in that they are not taught the rules directly, but instead taught how to learn and apply rules themselves. This requires much less feature engineering and direct involvement by researchers and developers.
How Deep Learning applies to NLP
Deep learning in NLP involves using machine learning algorithms and models such as convolutional neural networks (CNN) or recurrent neural networks (RNN) to learn the rules for language analysis, as opposed to being taught rules. Methods such as word embedding and sentiment analysis are applied to understand relations between words, semantics, and context through their association with related words. CNN's achieve this by tokenizing words into vector representations using look-up tables. These are run through layers of nodes that apply weights based on probabilistic intent that, through many different run-throughs, arrive at an optimal conclusion.
Why Deep Dearning is useful for NLP
Deep learning is particularly useful for NLP because it thrives on very large datasets. More traditional approaches to artificial language learning required a lot of data preprocessing of learning material, which requires human intervention. In addition to working well with extremely large datasets, deep learning is capable of identifying complex patterns in unstructured data, which is perfect for understanding natural language.
Unfortunately, deep learning requires large amounts of processing power, which historically limited its potential use. However, with the subsequent rise of cloud computing and big data, deep learning now has the infrastructure needed to thrive.
The use cases of NLP are virtually limitless, as they can be used for language comprehension, translation, and creation. A very practical example of this is chatbots, who are capable of comprehending questions given to them by customers in a natural language format. These chatbots can derive the intent and meaning behind a customer's request and produce unscripted responses based on the available information. Though they are generally only used as the first line of response currently, it demonstrates a very practical application of deep learning and NLP in the real world. Listed below are more general uses cases of NLP.
It goes without saying that translating text and speech into different languages is an extremely complicated process. Every language has its own unique grammatical constructions and word patterns. That is why translating texts or speech word by word often doesn’t work, as it can change the underlying style and meaning. Thanks to natural language processing, words and phrases can be translated into different languages while still retaining their intended meaning. Nowadays, Google Translate is powered by Google Neural Machine Translation, which can identify different language patterns with the help of machine learning and natural language processing algorithms. Also, machine translation systems are trained to understand terms related to a specific field such as law, finance, or medicine, for more accurate specialized translation.
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Whenever we type something on our computers we sometimes misspell, misplace or even miss out a word while writing an email or making a report. Thanks to one of the components of NLP systems, we are warned by red or blue underlines that we made a mistake. Automatic grammar checking notices and highlights spelling and grammatical errors within the text. One particularly popular example of this is Grammarly, which leverages NLP to provide spelling and grammatical error checking.
Part-of-speech tagging is another prominent NLP component that labels each word in a text with an appropriate part of speech (noun, verb, adjective, or adverb). It is useful for determining relationships within words and spotting specific language patterns. This task isn’t as simple as it may sound, as most words may have different parts of speech. For example, ‘rain’ can be both a noun and a verb depending on the context. Listed below are some examples of common part-of-speech tagging techniques:
Rule-Based Method: if a word ends with ‘ion’ or ‘er’, such as ‘station’ or ‘worker’, it must be assigned to a noun. If it ends with ‘ed’ or ‘ing’
Stochastic Method: it assigns POS tags based on how often a particular tag sequence occurred and how common it is
Identifying the right part of speech helps to better understand the meaning and subtext of sentences.
Automatic text condensing and summarisation
Automatic text condensing and summarization processes reduce the size of a text to a more succinct version. They preserve key information and rule out some phrases or words that either have no meaning or do not carry information critical to understanding the text. This application of natural language processing comes in handy when creating news digests or news bulletins and generating headlines.
Syntactic analysis is used to draw exact meaning from text. It checks whether a text conveys its meaning by comparing it to the grammatical rules. As an example of this, consider the sentence: swim go pool in Jack. This sentence is grammatically incorrect and it simply doesn’t make sense, despite containing all of the components required to generate a grammatically correct sentence. Syntactic analysis is used to show us whether the given sentence conveys its logical meaning and whether it’s grammatically correct.
Natural language processing has many practical applications in the real world, but empowering machines to make sense of natural language and to be able to generate novel text is an incredibly difficult task. Most human languages obey a set of rules, but likewise, most human languages also have irregularities and exceptions to these rules. In addition to this, there can be meaning in what is not said, an additional context that can change the meaning of the text, and intentional ambiguity.
All of this makes it very difficult and labor-intensive for humans to directly teach machines to understand natural language. Instead, deep learning empowers machines with the ability to derive rules and meaning from text by itself, with the help of extremely large datasets and powerful processors. This leads to many practical applications for deep learning and NLP, including chatbots, translation services, and text generation.