Best Natural Language Processing (NLP) Tools/Platforms (2023) (2023)

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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.

NLTK

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.

Stanford CoreNLP

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.

SpaCy

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

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.

Apache OpenNLP

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.

Google Cloud

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.

Text Blob

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.

Amazon Comprehend

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.

IBM Watson

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.

AllenNLP

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.

BERT

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.

GenSim

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.

Word2Vec

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.

CogCompNLP

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.

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Prathamesh Ingle

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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

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FAQs

Best Natural Language Processing (NLP) Tools/Platforms (2023)? ›

The future of NLP is to have machines that can understand and have a general understanding of human language. This would allow us to interact with machines in ways that we do with other humans. Natural Language Processing is a term that has been around for decades and has become an everyday part of our lives.

Which company is best at NLP? ›

Top Natural Language Processing Companies 2022
  • Google.
  • Wordsmith.
  • Indata Labs.
  • IBM.
  • Synthesia.
  • Intel.
  • MindMeld.
  • Microsoft.
Sep 22, 2022

Does NLP have a future? ›

The future of NLP is to have machines that can understand and have a general understanding of human language. This would allow us to interact with machines in ways that we do with other humans. Natural Language Processing is a term that has been around for decades and has become an everyday part of our lives.

Which NLP model gives the best accuracy? ›

Naive Bayes is the most precise model, with a precision of 88.35%, whereas Decision Trees have a precision of 66%.

Is NLP still popular? ›

Decision intelligence. While NLP will be a dominant trend in analytics over the next year, it won't be the only one. One that rose to prominence in 2022 and is expected to continue gaining momentum in 2023 is decision intelligence.

Why is NLP processing so difficult? ›

Why is NLP difficult? Natural Language processing is considered a difficult problem in computer science. It's the nature of the human language that makes NLP difficult. The rules that dictate the passing of information using natural languages are not easy for computers to understand.

Can you make money with NLP? ›

💰 Monetizing Your NLP Skills

The first step in making passive income with NLP is to monetize your skills. This can be done in a variety of ways, such as creating and selling NLP-based products, providing NLP consulting services, or even developing and licensing NLP software.

Who are the top NLP players? ›

The top players in the Natural Language Processing market are IBM, Microsoft, Google, AWS, Meta, 3M, Apple, SAS, Oracle, and Health Fidelity.

Is NLP high paying? ›

Highest salary that a NLP Engineer can earn is ₹20.0 Lakhs per year (₹1.7L per month). How does NLP Engineer Salary in India change with experience? An Entry Level NLP Engineer with less than three years of experience earns an average salary of ₹7.7 Lakhs per year.

What is the future of NLP 2023? ›

In 2023, we can expect to see the development of multilingual NLP systems that can understand and process languages from around the world. Improved Chatbots: Chatbots are becoming increasingly popular in industries such as customer service and e-commerce.

What do psychologists think of NLP? ›

Neuro-linguistic programming (NLP) is a coaching methodology devised in the 1970s by Richard Bandler, John Grinder and Frank Pucelik. However, many research scientists, psychologists, educators and even medical doctors have been intensely critical of NLP, with some even referring to it as “pseudoscientific rubbish.”

What is the most powerful NLP model? ›

GPT-3 (Generative Pre-Trained Transformer 3) is a neural network-based language generation model. With 175 billion parameters, it's also one of the largest and most powerful NLP language models created to date.

What model is better than BERT? ›

XLNet is a large bidirectional transformer that uses improved training methodology, larger data and more computational power to achieve better than BERT prediction metrics on 20 language tasks.

What neural network is best for NLP? ›

Convolutional neural networks (CNNs) have an advantage over RNNs (and LSTMs) as they are easy to parallelise. CNNs are widely used in NLP because they are easy to train and work well with shorter texts. They capture interdependence among all the possible combinations of words.

What are the new trends in NLP? ›

What are the latest trends in Natural Language Processing (NLP)? The latest trends in NLP include pre-trained language models, transfer learning, zero-shot learning, conversational AI, and multimodal fusion of speech and text data.

Do therapists use NLP? ›

Therapists may employ NLP techniques to identify their client's specific PRS and adjust their communication style and therapeutic goals accordingly. Some common NLP techniques include but are not limited to: Anchoring: Associating an external or internal trigger with a healthier response until it becomes automatic.

What are the latest breakthroughs in NLP? ›

One of the most significant advancements in NLP has been the development of language models like OpenAI's GPT-3. These models are trained on massive amounts of text data and can generate human-like language, enabling applications like chatbots, virtual assistants, and automated content creation.

Why is NLP outdated? ›

There is no scientific evidence supporting the claims made by NLP advocates, and it has been called a pseudoscience. Scientific reviews have shown that NLP is based on outdated metaphors of the brain's inner workings that are inconsistent with current neurological theory, and contain numerous factual errors.

What is the main problem of NLP? ›

The main challenge of NLP is the understanding and modeling of elements within a variable context. In a natural language, words are unique but can have different meanings depending on the context resulting in ambiguity on the lexical, syntactic, and semantic levels.

What is the success rate of NLP? ›

When the cost of the practitioner's time is taken into account, the breakeven point of offering an NLP service to patients is calculated to be a success rate of 35% (Figure 3).

What is the hourly rate for NLP? ›

$48.08 is the 25th percentile. Wages below this are outliers. $79.81 is the 75th percentile. Wages above this are outliers.

Does NLP require a lot of math? ›

Like other aspects of ML, natural language processing requires a deep understanding of mathematics, including probability, statistics, linear algebra and calculus.

What is the average salary of NLP researcher? ›

$138,914

Who is the best NLP coach in the world? ›

RIDHIMA DUA. Ridhima Dua is a Neuro-Linguistic programming (NLP) Expert, Life Coach, Corporate Trainer, Mentor, Leadership Coach and Motivational Speaker. She is also Associate Certified Coach (ACC, Credentials ICF, US), ICF Member and a certified NLP Classic Code & New Code NLP Trainer from NLP Academy London.

Who are NLP Logix competitors? ›

Who are NLP Logix 's competitors? Alternatives and possible competitors to NLP Logix may include Plotly , Digital Reasoning , and Atlas AI .

What is an NLP researcher? ›

What Is an NLP Scientist? An NLP scientist is responsible for the technical creation and coding of NLP devices and applications. Ultimately, these professionals provide machines with the ability to understand human languages.

Is NLP in demand? ›

The demand for NLP engineers has skyrocketed in recent years due to the growing use of voice-activated personal assistants, chatbots, and other forms of natural language interfaces.

Do you need a degree for NLP? ›

Many NLP engineers come from an academic background. An associate or bachelor's degree in engineering, data science or computer science is typically preferred. If a bachelor's degree isn't mandatory, a certain amount of experience may be required (including the completion of some NLP courses).

What is the salary of Python NLP? ›

How Much Do Python NLP Jobs Pay per Year? $123,000 is the 25th percentile. Salaries below this are outliers.

Are NLP courses worth it? ›

Yes – if you're curious about exploring communication and influence, and genuinely want to improve your life, and if you are prepared to put in the work to do so. NLP is particularly effective if you want to move forward to the next stage of your life journey.

What are the 2 main areas of NLP? ›

Techniques and methods of natural language processing. Syntax and semantic analysis are two main techniques used with natural language processing. Syntax is the arrangement of words in a sentence to make grammatical sense. NLP uses syntax to assess meaning from a language based on grammatical rules.

Is NLP the future of AI? ›

The constant improvement of cognitive interaction with humans due to enhanced Natural Language Processing (NLP) capabilities is a staple in defining the future of artificial intelligence. These progressions will lead to agile applications with improved coding that enable machines to communicate like humans.

What are the 3 pillars of NLP? ›

The 4 “Pillars” of NLP

As the diagram below illustrates, these four pillars consist of Sensory acuity, Rapport skills, and Behavioural flexibility, all of which combine to focus people on Outcomes which are important (either to an individual him or herself or to others).

Does NLP help mental health? ›

NLP can also be used to help individuals to move away from unhelpful behavior or habits and to develop healthier coping strategies. It can also be used to help individuals to manage stress, anxiety and depression. Overall, Neuro-Linguistic Programming can be a powerful tool for improving mental health.

What problems can NLP solve? ›

Solve Real-world Text Analytics Problems With NLP! Natural language processing (NLP) helps machines analyze text or other forms of input such as speech by emulating how the human brain processes languages like English, French, or Japanese.

What are the 5 steps in NLP? ›

Top 5 Natural Language Processing Phases
  • Lexical Analysis.
  • Syntactic Analysis.
  • Semantic Analysis.
  • Discourse Analysis.
  • Pragmatic Analysis.
  • Talk To Our Experts!
Feb 3, 2023

Why is NLP powerful? ›

Because NLP techniques focus on making behavioral changes, they can be used for a variety of different goals. Mental health professionals use NLP by itself or with other types of therapy, like talk therapy or psychoanalysis, to help treat depression and anxiety.

Why NLP is more difficult than artificial language processing? ›

Natural language processing (NLP) is a branch of artificial intelligence within computer science that focuses on helping computers to understand the way that humans write and speak. This is a difficult task because it involves a lot of unstructured data.

What are the 7 levels of NLP? ›

There are seven processing levels: phonology, morphology, lexicon, syntactic, semantic, speech, and pragmatic.

What are the six modalities of NLP? ›

Submodalities in NLP are fine distinctions or the subsets of the Modalities (Visual , Auditory , Kinesthetic, Olfactory , Gustatory , and Ad) that are part of each representational system that encode and give meaning to our experiences.

Why T5 is better than BERT? ›

The main difference between Bert and T5 is in the size of tokens (words) used in prediction. Bert predicts a target composed of a single word (single token masking), on the other hand, T5 can predict multiple words as you see in the figure above. It gives the model flexibility in terms of learning the model structure.

What is the most powerful language model? ›

The Most Important Large Language Models (LLMs) in 2023
  1. BERT by Google. Summary. ...
  2. GPT-3 by OpenAI. Summary. ...
  3. LaMDA by Google. Summary. ...
  4. PaLM by Google. Summary. ...
  5. LLaMA by Meta AI. Summary. ...
  6. GPT-4 by OpenAI. Summary.
Apr 11, 2023

Is spaCy better than BERT? ›

BERT gives an average error reduction of 45% over our simpler spaCy models. Because of its small training set, our challenge is extremely suitable for transfer learning.

Which is better BERT or RoBERTa? ›

RoBERTa, or Robustly Optimized BERT Pretraining Approach, is an improvement over BERT developed by Facebook AI. It is trained on a larger corpus of data and has some modifications to the training process to improve its performance.

What is the best optimizer for NLP? ›

Optimization algorithm Adam (Kingma & Ba, 2015) is one of the most popular and widely used optimization algorithms and often the go-to optimizer for NLP researchers. It is often thought that Adam clearly outperforms vanilla stochastic gradient descent (SGD).

Which algorithm is best for NLP? ›

The most popular supervised NLP machine learning algorithms are:
  • Support Vector Machines.
  • Bayesian Networks.
  • Maximum Entropy.
  • Conditional Random Field.
  • Neural Networks/Deep Learning.

Which programming language is best for NLP? ›

Although languages such as Java and R are used for natural language processing, Python is favored, thanks to its numerous libraries, simple syntax, and its ability to easily integrate with other programming languages. Developers eager to explore NLP would do well to do so with Python as it reduces the learning curve.

Is Python good for NLP? ›

There are many things about Python that make it a really good programming language choice for an NLP project. The simple syntax and transparent semantics of this language make it an excellent choice for projects that include Natural Language Processing tasks.

What are the best optimizers for NLP? ›

Optimization algorithm Adam (Kingma & Ba, 2015) is one of the most popular and widely used optimization algorithms and often the go-to optimizer for NLP researchers. It is often thought that Adam clearly outperforms vanilla stochastic gradient descent (SGD).

What software does NLP technology work with? ›

Natural Language Toolkit (NLTK) is a suite of libraries for building Python programs that can deal with a wide variety of NLP tasks. It is the most popular Python library for NLP, has a very active community behind it, and is often used for educational purposes.

What is best NLP in Python? ›

Top NLP Libraries
  • Natural Language Toolkit (NLTK) NLTK is one of the leading platforms for building Python programs that can work with human language data. ...
  • Gensim. ...
  • CoreNLP. ...
  • spaCy.

Is there a better optimizer than Adam? ›

SGD is better? One interesting and dominant argument about optimizers is that SGD better generalizes than Adam. These papers argue that although Adam converges faster, SGD generalizes better than Adam and thus results in improved final performance.

Which NLP algorithms are best for sentiment analysis? ›

RNNs are probably the most commonly used deep learning models for NLP and with good reason. Because these networks are recurrent, they are ideal for working with sequential data such as text. In sentiment analysis, they can be used to repeatedly predict the sentiment as each token in a piece of text is ingested.

Which optimization technique is best? ›

The gradient descent method is the most popular optimisation method. The idea of this method is to update the variables iteratively in the (opposite) direction of the gradients of the objective function.

What are the 7 layers of NLP? ›

There are seven processing levels: phonology, morphology, lexicon, syntactic, semantic, speech, and pragmatic.

Which deep learning framework is best for NLP? ›

The most popular frameworks for Natural Language Processing (NLP) tasks are listed here without further ado.
  • NLTK. ...
  • Stanford CoreNLP. ...
  • SpaCy. ...
  • GPT-3. ...
  • Apache OpenNLP. ...
  • Google Cloud. ...
  • Text Blob. ...
  • Amazon Comprehend.
Apr 14, 2023

Which deep learning model is best for NLP? ›

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. GPT2: Language Models Are Unsupervised Multitask Learners. XLNet: Generalized Autoregressive Pretraining for Language Understanding. RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Is Google using NLP? ›

Our systems are used in numerous ways across Google, impacting user experience in search, mobile, apps, ads, translate and more. Our work spans the range of traditional NLP tasks, with general-purpose syntax and semantic algorithms underpinning more specialized systems.

What are the four applications of NLP? ›

Here are a few prominent examples.
  • Email filters. Email filters are one of the most basic and initial applications of NLP online. ...
  • Smart assistants. ...
  • Search results. ...
  • Predictive text. ...
  • Language translation. ...
  • Digital phone calls. ...
  • Data analysis. ...
  • Text analytics.

Is Amazon Lambda an NLP engine? ›

Discover insights and relationships in text Amazon Comprehend is a natural language processing (NLP) service that uses… AWS Lambda: A serverless computing service, that allows developers to run code without managing or provisioning servers.

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