An essential area of artificial intelligence is natural language processing (NLP). The widespread use of smart devices (also known as human-to-machine communication), improvements in healthcare using NLP, and the uptake of cloud-based solutions are driving the widespread adoption of NLP in the industry. But what is NLP exactly, and why is it significant?
Linguistics, computer science, and artificial intelligence all meet in NLP. A good NLP system can comprehend documents’ contents, including their subtleties. Applications of NLP analyze and analyze vast volumes of natural language data—all human languages, whether spoken in English, French, or Mandarin, are natural languages—to replicate human interactions in a human-like manner.
Why is NLP so essential?
We depend on machines more than ever since they allow us to be considerably more productive and accurate than we could ever be. However, there is a significant challenge with NLP activities. They are not worn out. They are uncomplaining. They are never bored.
The uniqueness of natural language and the uncertainty of languages make NLP a difficult area to work with. It is relatively easy for humans to learn a language, but it is quite difficult for machines to understand natural language. To provide structure to data deemed unstructured (i.e., Contrary to a record of a store’s transaction history, the text lacks a schema), we must first identify a solution that addresses the problems of linguistic creativity and ambiguity problems.
Tools for NLP projects
Many open-source programs are available to uncover insightful information in the unstructured text (or another natural language) and resolve various issues. Although by no means comprehensive, the list of frameworks presented below is a wonderful place to start for anyone or any business interested in using natural language processing in their projects. The most popular frameworks for Natural Language Processing (NLP) tasks are listed here without further ado.
Natural Language ToolKit is one of the leading frameworks for developing Python programs to manage and analyze human language data (NLTK). The NLTK documentation states, “It offers wrappers for powerful NLP libraries, a lively community, and intuitive access to more than 50 corpora and lexical resources, including WordNet.” It also offers a suite of text-processing libraries for categorization, tokenization, stemming, tagging, parsing, and semantic reasoning.
Learning NLTK takes time, just like learning most things in programming. The book Natural Language Processing with Python, produced by the NLTK designers themselves, is one of many books available to help you in your quest to understand the framework. It provides a very useful method for writing code to solve Natural Language Processing issues.
The Stanford NLP community created and actively maintains the CoreNLP framework, a well-liked library for NLP activities. NLTK and SpaCy were written in Python and Cython, respectively, whereas CoreNLP was written in Java, requiring JDK on your machine (but it does have APIs for most programming languages).
The creators of CoreNLP refer to it as “your one-stop shop for natural language processing in Java!” on the website. Token and sentence borders, parts of speech, named entities, numerical and time values, dependency and constituency parser, sentiment, coreference, quote attributions, and relations are just a few of the linguistic annotations that may be derived for text by using CoreNLP. Arabic, Chinese, English, French, German, and Spanish are among the six languages that CoreNLP currently supports.
The fact that CoreNLP is highly scalable makes it a top choice for difficult tasks, which is one of its key advantages. It was designed with speed in mind and has been tweaked to be exceptionally quick.
It is a library that may be used with both Python and Cython. It is a development of NLTK that incorporates word vectors and pre-trained statistical models. Tokenization is now supported for more than 49 languages.
This library can be regarded as one of the best for working with tokenization. The text can be broken into semantic units like words, articles, and punctuation.
All of the functionality needed for projects in the real world is present in SpaCy. Of all the NLP software now on the market, it also boasts the quickest and most precise syntactic analysis.
GPT-3 is a new tool that Open AI recently released. It is sturdy while also being fashionable. Since text prediction is its primary usage, it is an autocompleting application. GPT-3 will generate something similar but distinctive based on several instances of the desired text.
Open AI is always working on the GPT project. The third version is nice. One huge advantage is the enormous amount of data it was pre-trained on (175 billion parameters). If you employ it, you can produce more similar results to spoken language.
Accessibility is crucial when using a tool for extended periods, yet it is tough to find in open-source natural language processing technology. Despite having the required capability, it might be too challenging to utilize.
Apache OpenNLP is an open-source library for people who value practicality and accessibility. Like Stanford CoreNLP, it uses Python decorators and Java NLP libraries.
OpenNLP is a simple but effective tool in contrast to the cutting-edge libraries NLTK and Stanford CoreNLP, which have a wealth of functionality. It is among the finest solutions for named entity recognition, sentence detection, POS tagging, and tokenization. Additionally, you can modify OpenNLP to meet your needs and eliminate unnecessary features.
The Google Cloud Natural Language API offers several pre-trained models for sentiment analysis, content categorization, and entity extraction. AutoML Natural Language is another feature that enables you to build custom machine learning models.
It uses Google’s question-answering and language-comprehension tools as part of the Google Cloud architecture.
It is the market’s quickest machine-learning tool. Another readily accessible NLTK-based natural language processing tool is Text Blob. This might be enhanced with extra features that allow for more textual information.
Text Blob sentiment analysis can be used for customer contact through speech recognition. Additionally, you may develop a model using a trader’s linguistic expertise from Big Business.
Standardizing content is becoming usual and advantageous. It would be great if your website or application could be automatically localized. A machine translation feature in Text Blob is another helpful feature. To enhance machine translation, use the Text Blob language text corpora.
The Amazon Web Services architecture includes the natural language processing (NLP) service Amazon Comprehend. Sentiment analysis, topic modeling, entity recognition, and other NLP applications can all be made using this API.
From emails, social media feeds, customer service tickets, product reviews, and other sources, it extracts relevant information from text. Extracting text, keywords, subjects, sentiment, and additional information from documents like insurance claims may help simplify document processing operations.
A group of artificial intelligence (AI) services known as IBM Watson are housed on the IBM Cloud. Natural language understanding is one of its important features, which enables you to recognize and extract words, groups, emotions, entities, and more.
It’s flexible since it can be adjusted to various industries, from banking to healthcare, and it includes a library of papers to get you started.
Strong text preprocessing abilities in a prototyping tool. SpaCy is more production-optimized than AllenNLP, but research uses AllenNLP more frequently. Additionally, it is powered by PyTorch, a well-liked deep-learning framework that offers far more flexibility for model customization than SpaCy.
Bidirectional Encoder Representations from Transformers are known as BERT. It is a pre-trained Google algorithm created to predict what users want more accurately. Contrary to earlier contextless methods like word2vec or GloVe, BERT considers the words immediately adjacent to the target word, which might obviously change how the word is interpreted.
The canon is a collection of linguistic data. Regardless of the size of the corpus, it has a variety of methods that may be applied. A Python package called Gensim was made with information retrieval and natural language processing in mind. This library also features outstanding memory optimization, processing speed, and efficiency. Before installing Gensim, NumPy and SciPy, two Python packages for scientific computing, must be installed because they are required by the library.
A word is represented as a vector by word embedding. Using their dictionary definitions, words are transformed into vectors that may be used to train machine learning (ML) models to recognize similarities and differences between words. An NLP tool for word embedding is called Word2Vec.
A tool created at the University of Pennsylvania is called CogCompNLP. It is available in Python and Java for processing text data and can be stored locally or remotely. Some of its features are tokenization, part-of-speech tagging, chunking, lemmatization, semantic role labeling, etc. Big data and remotely stored data are both workable with it.
Prathamesh Ingle is a Mechanical Engineer and works as a Data Analyst. He is also an AI practitioner and certified Data Scientist with an interest in applications of AI. He is enthusiastic about exploring new technologies and advancements with their real-life applications