Natural language processing (NLP) has become a part of our daily lives. It now plays a crucial role in simplifying once-time consuming tasks. A perfect example is sending a voice command to a smartphone, a virtual home assistant, or even a car to get a task done. Voice-enabled tools, including popular ones like Google Assistant, Alexa, and Siri, all use NLP and machine learning (ML) to function properly.
In this article, we’ll be exploring the association between NLP and machine learning, the important libraries for NLP, and how deep learning can take NLP even further.
Machine learning, natural language processing, and artificial intelligence (AI) are often used interchangeably. However, there are significant differences among the three.
AI is a branch of computer science that lets computers learn and accomplish tasks previously handled by humans.
Machine learning is a part of AI that gives computers the capability to learn and become better from experience without the need to be explicitly programmed again.
Natural language processing is also a type of AI that gives systems the ability to read, understand, and interpret human language. This means machines can make sense of spoken or written text and execute tasks such as sentimental analysis, automatic text summarization, and speech recognition.
Generally speaking, natural language incorporates human communication – including the way humans talk and the way spoken words are used in day-to-day life. But processing natural language is a challenging task for machines since there are a number of factors that affect the way humans interact with each other and their environment. The rules are very few and fragmented and can vary depending on the specific language and the dialect, the context of the conversation, and the relationship of those speaking.
NLP uses machine learning to enable a machine to understand how humans communicate with one another. It also leverages datasets to create tools that understand the syntax, semantics, and the context of a particular conversation. Today, NLP powers much of the technology that we use at home and in business.
Machine learning utilizes learning models to command its understanding of human language. It is based on a learning framework that lets computers train themselves on input data. ML can use a wide range of models to process data to facilitate better understanding. It can interpret standard and unusual inquiries. And because it can improve continually from experience, it can also handle edge cases independently without being reprogrammed.
There is sometimes a confusion over the association between machine language and natural language processing. ML can be applied in NLP technology, but there are several types of NLP that function without relying on AI or ML. A good example is an NLP tool that is designed to simply extract basic data. It may rely on systems that do not need to learn continually through AI.
However, for more intricate applications of machine learning NLP, systems can use ML models to enhance their understanding of natural speech. ML models can also make it easier to adjust to modifications in human language over time. NLP, meanwhile, can use unsupervised machine learning, supervised machine learning, both, or neither along with other systems to power its applications.
When used in natural language processing, machine learning can identify patterns in human speech, understand sentient context, pick up contextual clues, and learn any other component of the text or voice input. More complex applications that need high-level understanding to have a comprehensible conversation with people require ML to make it possible.
Machine learning for NLP encompasses a series of arithmetical systems to identify different sections of speech, sentiment, entities, and other text aspects. These systems can be in the form of a model that can be applied to other sets of texts in what is known as supervised machine learning. In addition to being a model, the systems can also be a series of algorithms that function across large datasets to extract meaning in what is called unsupervised machine learning.
When dealing with NLP in machine learning, it is vital to understand the main dissimilarity between supervised learning and unsupervised learning. This way, it is easier to obtain the best out of both in just one system.
NLP text data needs a unique attitude to machine learning because text data may have thousands of dimensions, including phrases and words, but may be sparse. As an example, the English language (Oxford English Dictionary) has slightly over 170,000 words in use. But a single tweet can comprise only several dozen.
In supervised ML, a huge amount of text is annotated or tagged with samples of what the system should look for as well as how it should interpret it. These texts are used to teach a statistical model that is assigned un-tagged text to examine. Later, larger or even better datasets can be used to retrain the model since it learns more about the text it examines. For instance, you can utilize supervised machine learning to train a specific model to examine film or TV show reviews and later teach it to incorporate the star rating of each reviewer.
It is crucial that the data or information fed to the model is accurate and clean. This is because supervised machine learning only functions with quality input otherwise it will not produce the required results. With enough training, tagged data is fed through the model and the machine examines the text and evaluates it based on what it has learned from the samples.
This form of NLP machine learning uses statistical models to power its understanding. It becomes more precise over time and data scientists can broaden the textual data the machine interprets as it continually learns. However, this ML use case has some challenges in terms of comprehending edge cases since machine learning NLP in this context depends heavily on statistical simulations.
The precise approaches used by data scientists to train machines vary from one application to another, but the following are the main methods:
Categorization: The machine is taught about the important and overarching groups of content. Simulation of this data enables a deeper understanding of the text context.
Tokenization: The text is distilled into separate words or tokens that allow the machine to classify the keywords used in the text before processing the data.
Classification: This technique identifies the category that the text data is contained in.
Sentiment analysis: This category explores the tone of the particular text data. It examines the feelings behind the text and assigns them negative, neutral, or positive tones.
Part-of-speech tagging: This is similar to diagramming English sentences. However, in this case, it is for NLP machine learning.
Named entity recognition: After feeding the system individual words, a data scientist identifies important entities, such as proper nouns.
Unsupervised machine learning involves training a particular model without annotating or pre-tagging. This type of ML can be tricky but it is far less data- and labor-intensive compared to supervised ML.
There are different kinds of unsupervised machine learning systems, but here are the three most common:
Matrix factorization: With this model, the system explores latent factors in specific data matrices which can then be defined in various techniques and are based on analogous characteristics.
Clustering: The system groups similar documents into sets. It then looks at the pyramid of information and sorts it based on relevance and importance.
Latent semantic indexing (LSI): It involves identifying phrases or words that regularly occur together. Developers use LSI for faceted searches and for returning search queries that are not an exact search phrase.
This model usually comes up in discussions on search engine optimization and search engines in general. It comes into practice when Google recommends search results, including contextually similar words.
While there are many libraries that can be used for NLP projects, the following are among the most popular and commonly used.
NLTK is one of the top frameworks for creating Python applications that can operate on human language data. Sentence identification, tokenization, lemmatization, stemming, parsing, chunking, and POS tagging are just a few of the text processing functions that it has. Over 50 corpora and lexical resources can be accessed through NLTK's user-friendly interfaces.
Python's spaCy is an open-source NLP package. It allows you to create applications that process massive amounts of text because it is specifically intended for use in production environments. It can be used to build information extraction or natural language processing systems. It has word vectors and pre-trained statistical models, and can accommodate more than 49 languages for tokenization.
TextBlob provides very convenient APIs for standard NLP tasks, including POS tagging, noun phrase extraction, sentiment analysis, classification, language translation, word inflection, parsing, n-grams, and WordNet integration. The objects it creates can be thought of as Python strings with NLP training.
Since CoreNLP is developed in Java, a device must have Java installed. The library does, however, provide programming interfaces for a number of well-known languages, such as Python. Numerous NLP technologies from Stanford are included in the tool, such as named entity recognizer (NER), part-of-speech tagger, sentiment analysis, bootstrapped pattern learning, and coreference resolution system. In addition, CoreNLP supports Arabic, Chinese, German, French, and Spanish languages.
Image source: LaptrinhX
Deep learning (DL) is usually mentioned frequently in conversations about machine learning and natural language processing. The term refers to a system based on simulating human brain function through an extensive neural network. Deep learning is usually applied to expand on ML systems, work with complex NLP use cases, and deal with continually increasing datasets.
Deep learning is so called because it looks deeper into data compared to standard ML methods. Instead of getting a shallow understanding of the data, it produces comprehensive results that are also easily scalable.
Unlike machine learning, DL does not collapse when it is made to learn and improve over time. It begins by learning basic concepts and improves on this experience to scale into more intricate ones. This makes it ideal for developing the complex understanding that is required for high-level NLP projects.
Recently, there has been renewed curiosity about NLP machine learning and NLP deep learning due to the ease with which deep learning and machine learning algorithms can be applied. This is why nearly all DL algorithms - autoencoders, deep neural networks, recurrent neural networks, convolutional neural networks, and restricted Boltzmann machines - have been studied to achieve greater precision in different implementations of NLP.
We have seen how machine learning serves as a crucial value addition in most NLP applications. When combined with it, NLP becomes a very helpful tool to carry out difficult natural language-related tasks like dialogue generation and machine translation. Some of the areas of NLP where machine learning and deep learning are applied with positive results include sentiment analysis, question answering systems, chatbots, and information retrieval systems, to name a few. It will be interesting to see what the future holds for these technologies, and NLP, in particular.
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