Natural language processing (NLP) can help people explore deep insights into the unformatted text and resolve several text analysis issues, such as sentiment analysis and topic classification. NLP is a field of artificial intelligence (AI) that uses linguistics and coding to make human language comprehensible to devices. NLP can help find in-depth information quickly by using a computer to assess data.
So what if a software-as-a-service (SaaS)-based company wants to perform data analysis on customer support tickets to better understand and solve issues raised by clients? The capacity of their human team is limited. For instance, the average Zendesk implementation deals with 777 customer support tickets monthly through manual processing.
Enter NLP. They company could use NLP to help segregate support tickets by topic, analyze issues, and resolve tickets to improve the customer service process and experience.
The NLP market was valued at $13 billion in 2020 and is expected to increase at a compound annual growth rate (CAGR) of 10% from 2020 to 2027, estimated to reach around $25 billion. The tech and telecom industries are leading demand with a 22.% share with NLP, followed by the banking, financial service, and insurance (BFSI) industry.
NLP providers work on the AI technology that optimizes the analysis of language-based information. However, how do you choose the best solution for your business with so many providers?
See below to learn all about NLP technology and the top NLP providers in the market:
Choosing the right natural language processing provider
Top natural language processing providers
IBM offers several emerging technologies, including AI. IBM Watson is empowered with AI for businesses, and a significant feature of it is natural language, which helps users identify and pick keywords, emotions, segments, and entities. It makes complicated NLP obtainable to company users and enhances team member yield.
Using the IBM Watson Natural Language Classifier, companies can classify text using personalized labels and get more precision with little data. It further supports multiple languages.
- Extract insights from text and understand sentiments
- Ease of use
- Text classification into different custom categories
- Add automation to workflow
- Versatile and customizable based on niche
The API is available for free for 30 days with IBM Cloud. After that, the user could pick a paid plan according to their requirements on a pay-as-you-go basis.
Google Cloud, a pioneer of language space, offers two types of NLPs, Auto Machine Learning and Natural Language API, to assess the framework and meaning of a text. Google focuses on the NLP algorithm used across several fields and languages. It refines user experience in ads, searching, and translation.
Google NLP API uses Google’s ML technologies and delivers beneficial insights from unstructured data. It offers entity recognition, sentiment assessment, syntax evaluation, and content segmentation in 700 groups. It offers text analysis in several languages, including English, German, and Chinese.
- Ease of use
- Offers sentiment analysis of products on e-ommerce websites
- Derives meaning from audio files
- Derives information from blogs, articles, and documents
Google prices NLP according to the units used. Each document evaluated by the API is one unit. You can process 5,000 units monthly for free. After that, you will be charged depending on the features used. For every 1,000 units, you will be charged $1 per month.
AWS’ Amazon Comprehend is the NLP service that uses ML to analyze the framework and significance of the content. The service can help differentiate the text’s essential elements, including location, language, and event.
Amazon Comprehend offers Custom Entity Recognition, Sentiment Analysis, and custom segmentation to help users integrate NLP into their apps. It can identify text written in more than 100 languages.
- Ease of use
- Knowledge management and discovery
- Voice of customer analytics
- Secure SSL connection
- Special Amazon Comprehend Medical for health care industry
The free tier of 12 months begins from the first request submitted. NLP processing requests are measured in units of 100 characters, and every unit is 100 characters. Users are charged accordingly.
Microsoft has a devoted NLP section that stresses developing operative algorithms to process text information that computer applications can contact. It also assesses glitches like extensive vague natural language programs, which are difficult to comprehend and find solutions.
It offers text classification, text summarization, embedding, sentiment analysis, sentence similarity, and entailment services. Microsoft Azure AI supports multiple languages besides English.
- Broad entity recognition
- Processing medical text
- Azure ML designer
- Support for data labeling projects
Sentiment analysis, language detection, and customized question answering are free for 5,000 text records per month. After that, the pricing is $1 per 1,000 text records.
Intel offers an NLP framework with helpful design, including novel models, neural network mechanics, data managing methodology, and needed running models. The company worked with AbbVie to form Abbelfish Machine Translation for language translator facilities developed on the NLP framework with the help of Intel Xeon Scalable processing units.
NLP Architect by Intel helps explore innovative deep learning techniques to streamline NLP and NLU neural networks. Intel NLP offers sturdy extraction of linguistic features for better NLP. It offers semantic understanding and components for conversational AI.
- Conversational AI
- Discovering topics embedded in texts
- NLU modules
- End-to-end deep learning apps using new topologies
The Intel NLP Architect is free to explore deep learning techniques for NLP and NLU. Contact Intel for pricing on the Intel Xeon Scalable processing units.
MindMeld is a tech company based in San Francisco that developed a deep domain conversational AI platform, which helps companies develop conversational interfaces for different apps and algorithms.
Some of its use cases include food ordering technology, video discovery, and home assistance. The MindMeld NLP has all classifiers and resolvers to assess human language with a dialogue manager managing dialog flow.
It has previously been named one of the world’s 50 “most competent” companies of by MIT Technology Review and stood as of the top 100 “brilliant companies” of by Entrepreneur magazine.
- Training at all levels of NLP hierarchy
- Custom configurations
- Developing models incrementally
- Ease of installation
The MindMeld NLP API can be downloaded and installed for free. Contact MindMeld for post-trial pricing.
SoundHound, based in Santa Clara, California, develops technologies like speech and sound recognition, NLU, and search.
The goal of SoundHound is to allow humans to interact with what they like to do that’s around them.
Some of their products include SoundHound, a music discovery application, and Hound, a voice-supportive virtual assistant. The company also offers voice AI that helps people speak to their smart speakers, coffee machines, and cars.
- Audio and voice identification
- Creating personalized voice assistance
- Precision and speed
- Edge connectivity
Users can sign up with a free account trial and then pick up packages as they want to use the SoundHound NLP services.
Stanford CoreNLP is a library developed and maintained by the Stanford University NLP community.
To use it, users should install JDK on their computers. The CoreNLP toolkit helps users perform several NLP tasks, such as tokenization, entity recognition, and part-of-speech tagging.
CoreNLP can be used through the command line in Java code, and it supports eight languages.
- Efficient text data analysis
- Open source
- Regex engines
- Entity extraction
- Scalability and optimization
Users can download the CoreNLP server and install it. Contact Stanford for licensing for distributed commercial applications.
SpaCy is an open-source NLP with Python libraries.
It is efficiently documented and designed to support big data volume, including a series of pre-trained NLP models to simplify user jobs.
SpaCy has a short menu and serves the best accessible option available. The library helps craft text for deep learning and is good at extracting tasks. It supports English.
- Concise and user-friendly API
- Short and crisp menu
- Fast and precise syntactic analysis
- Ease to customize vectors
SpaCy is an open-source NLP library and is available for free.
Here are the top features to consider from NLP providers. There are some shallow and deep features that you will find:
Shallow means simple features that are easier to compute and do not need to be technical.
- Text length counting: Features like word length and character length counting are common.
- Non-dictionary word count: Counting or ratio of non-dictionary words or those words that are out of vocabulary.
- Readability metrics: Metrics such as SMOG or Flesch-Kincaid Readability Test are used. They are complex word ratios based on the aggregate number of words.
- Unique word ratios: Ratio of unique words to total words.
- Sentence type: Features like counting, Boolean variable for the availability of questions, punctuation, and exclamations.
- Emojis: Counting the presence of emojis in a text.
Deep features mean complex features. These features need high machine computation. They are difficult and time consuming, and you may use libraries for them.
- Part of speech: POS can be used to extract nouns, adjectives, verbs, and other words. Generate POS tags for sentences to define patterns in data.
- Named entity recognition (NER): NER helps identify names of people, companies, locations, percentages, and quantities.
- Sentiment analysis: Sentiments and emotions are part of a text. The sentiment is essential to translate the meaning of the text. Using libraries, NLPs compute sentiments. It is reasonably precise, and you may need a GPU for it.
- Sentence vectors: Similar to word vectors, sentences with vectors can also be transformed.
- Morphological and lexical analysis: Lexical analysis involves words and expressions. It includes assessment, identification, and description of the words.
- Semantic analysis: It is a structure developed by a syntactic assessor to give meaning. It transfers word sequences into structures, depicting how words are linked.
- Pragmatic analysis: It manages overall communication and social text and its impact on interpretation. It helps showcase the intended effect of a dialog.
- Syntax analysis: Syntax focuses on the correct order of the words. It includes assessments of words in a sentence keeping the grammatical structure focused.
Here’s a close look at how NLP technology can help companies:
Perform large-scale scrutiny
NLP offers text analysis at a massive scale on all types of documents, emails, reviews, social media data, blogs, articles, and more. Companies can process vast chunks of data within seconds or minutes that would need a long period of time for human analysis. Also, NLP tools can be scaled up or down according to your needs.
Objective and precise evaluation
When doing repetitive tasks, like reading or assessing survey responses, humans can make mistakes that hamper results. NLP tools are trained to the language and type of your business, customized to your requirements, and set up for accurate analysis.
Optimize processes and lower costs
NLP tools work round-the-clock at any scale in real-time. You don’t need a team to work full-time with NLP SaaS tools. When you link NLP with your data, you can assess customer feedback to know which customers have issues with your product. You can also optimize processes and free your employees from repetitive jobs.
Enhanced customer satisfaction
NLP allows users to automatically assess and resolve customer issues by sentiment, topic, and urgency and channel them to the required department, so you don’t leave the customers waiting. It helps automatically manage, route, and answer customer issues.
Better insight into market
NLP has a major impact on marketing. NLP understands your customer base’s language, offers better insight into market segmentation, and helps address your targeted customers directly.
NLP will remove repetitive and tedious work from your team, leading to boredom and fatigue. Your employees can focus on important work with automated processes and data analysis.
Accurate and actionable insights
The random data of open-ended surveys and reviews needs an additional evaluation. Users need to break down the text to help the machine understand it. But AI-supported NLP tools understand it. NLP allows users to dig into unstructured data to get instantly actionable insights.
Before choosing an NLP provider, review several NLP use cases to know how the technology can be applied:
One of the most evident uses of natural language processing is a grammar check. With the help of grammar checkers, users can detect and rectify grammatical errors. They also support language learning and text authoring. While you can still check your work for errors, a grammar checker works faster and more efficiently to point out grammatical mistakes and spelling errors and rectifies them. Writing tools such as Grammarly and ProWritingAid use NLP to check for grammar and spelling. These tools also check text clarity and suggest better synonyms.
Strong customer support is a key goal in business. Engaging employees to meet the demands of the customer is daunting. This is where chatbots enter. They are replacing round-the-clock human customer support. Using NLP to train chatbots to behave specifically helps them react and converse like humans. Users interacting with chatbots may not even realize they are not talking to a person. Chatbots have become more content-sensitive and can offer a better user experience to customers.
Autocorrect and autocomplete
Another major application of NLP is autocompleting. When you enter a search query in a search engine, you will notice several predictions of your interest depending on the first few letters or words. How does the search engine offer these suggestions? It depends on the data it collects from other users searching for the same terms. This is where NLP comes in. It helps to know the subtleties between different search terms. Autocorrect is also a service of NLP that rectifies the misspelled words to the closest right term.
Machine translations can replace dictionaries. Most machine language services can quickly translate millions of words. Businesses are using language translation tools to overcome language hurdles and connect with people across the globe in different languages.
Banking and finance
Banks can use sentiment analysis to assess market data and use that information to lower risks and make good decisions. NLP also helps companies check illegal activities, such as fraudulent behavior.
Insurance companies apply NLP to detect and reject fraudulent claims. Insurers can assess customer communication using ML and AI to detect fraud and flag those claims.
Manufacturers use NLP to assess information related to shipping to optimize processes and enhance automation. They can assess areas that need improvement and rectification for efficiency. NLP also scrutinizes the web to get information about the pricing of materials and labor for better costs.
Retailers use NLP to assess customer sentiment regarding their products and make better decisions across departments, from design to sales and marketing. NLP evaluates customer data and offers actionable insights to improve customer experience.
NLP assesses patient communication from chat, email, and files to help health professionals prioritize them depending on their needs. It also helps categorize patients based on the problem and treatment to drive better results.
What to look for in natural language processing providers?
Natural language processing has become a prominent part of human life. It is helping companies acquire information from unstructured text, such as email, reviews, and social media posts.
However, it is difficult to pick the right vendor with so many NLP providers. Users can choose from open-source and SaaS tools. Open-source libraries help developers customize a solution. An SaaS tool can be a good platform if you don’t want to invest in developing NLP infrastructure.
Different service providers are working on both these models. You can select the best provider, including their domain experience, to build your specific application around the automated processing and analysis of language.