The natural language processing (NLP) field has exploded in recent years, with data scientists and developers creating innovative ways to incorporate NLP into new business processes and functions. Today, it’s used for everything from improving the customer experience to quality assurance, risk and compliance management, business performance improvement, and everything in between.
Developers release an ever-growing number of sophisticated NLP software applications, and companies integrate NLP into their existing products at a rapid pace, resulting in an overwhelming number of NLP solutions to choose from. At the same time, the complexity of NLP makes it challenging for some businesses to understand how they can best leverage the technology to streamline their business processes.
In this article, we’ll review what natural language processing is and explore expert tips and best practices for choosing the best NLP software for your business.
What is natural language processing?
Natural language processing (NLP) is a type of artificial intelligence (AI) that enables computers to interpret and understand spoken and written human language. It uses machine learning (ML), deep learning, and analytics models, coupled with computational linguistics, or the application of computer science methodologies to human language, giving rise to capabilities like sentiment and emotion analysis, customer journey analytics, sophisticated business intelligence, and much more.
Computers are based on the binary number system, or the use of 0s and 1s, and can interpret and analyze data in this format, and structured data in general, easily. The goal of NLP is to enable humans to communicate with computers using natural human language and vice-versa. NLP does just that through a complex combination of analytical models and methods. NLP makes it possible to analyze unstructured data such as text in emails, social media posts, website content, chatbots, spoken language such as phone conversations, and importantly, the meanings and emotions behind that language.
For companies that want to adopt NLP, there are several options:
- Building your own
- Leveraging ow-code ML platforms and pre-trained models
- Purchasing NLP software with the capabilities your company needs
Let’s take a look at some of the pros and cons of these various approaches and what to consider when looking for the best NLP software.
Choosing the best NLP software: Key considerations
1. Choose NLP software that analyzes what’s there and what’s not there. “So much of a conversation is not what you hear, not what makes sense, but what you don’t or doesn’t. Without a deep set of experiences (relevant data) finding the anomalies or missing things is nearly impossible.
“Let me share some examples that a data scientist who is new to this world needs to figure out. These are all real, and all sadly, very common among clients. Did you know that the candy ‘Tootsie Rolls’, among others, has a hotline, and that hotline has no required prompts, so it is effectively an endless loop? This is important if an agent who is not really working hard wants to take an unscheduled break. Just dial that number and sit there looking busy. That is something an organization may want to find in a speech analytics system. Or agents listening to phone rings for 5 minutes, or listening to 10 minutes of an answering machine, or an internal extension to listen to hold music for half an hour.
Even more diabolical is silence. Is silence good or bad? That depends, that tiny blackhole in an audio recording is speech analytics gold, highly important, and not trivial data science either.” - 3 Potential Pitfalls of DIY Speech Analytics, CallMiner; Twitter: @CallMiner
2. Identify your use case and the complexity of your needs. “First, consider what you need from NLP software. Do you need a solution that will help you understand customer sentiment? Or maybe you need a tool that can help with summarizing complex text documents? The type of use case will influence which type of NLP software best fits your needs.
“Next, take into account the complexity of tasks you plan to accomplish. If you are planning on using NLP for more basic tasks, such as classification or summarization, then an out-of-the-box solution may be sufficient for your needs. However, if you are looking for a more advanced application requiring advanced machine learning techniques (e.g., deep learning), then you may require specialized solutions or services from a vendor who specializes in these types of solutions.” - Natural Language Processing Software, SourceForge; Twitter: @sourceforge
3. Determine whether your use case is internal or external. “Check which team will be using the NLP software—Is it going to be used entirely internally, such as by the finance team to consolidate financial agreements, or will it be used by a team such as the customer service department where external users will come into the picture. If you are purchasing it for an internal team, make sure it supports functions such as credit scoring and document search. If you want it for departments such as customer service, look for a solution with strong sentiment analysis capabilities.” - Find the best Natural Language Processing (NLP) Software, Software Advice; Twitter: @SoftwareAdvice
4. Implement a strategy that leverages APIs. “For most enterprises, the best approach to leveraging NLP and becoming a natural language-enabled enterprise would be a strategy that includes APIs. That is — provided that the vendor has enabled the capability for the customer to easily tune and optimize its general-purpose model so it can work on customer data. This would save enterprises tens of millions of dollars every year and accelerate time-to-value.” - Ryan Welsh, How to choose the right NLP solution, VentureBeat; Twitter: @VentureBeat
5. Out-of-the-box solutions typically are trained on general-purpose data sources. “When you have limited time or you lack the data to train an NLP model, an out-of-the-box solution offers a couple of major advantages. It’s effective for quick proofs of concept and delivers high returns on investment. This is especially true when NLP models need to understand higher-level items like categories of concepts within documents.
“One thing to remember: Prebuilt NLP models are often trained on general-purpose data sources. As a result, they tend not to be tailored to any single industry or domain, which often requires the understanding of specialized terms and intentions.” - Custom, or Out of the Box? How to Choose the Right NLP Model for Your Enterprise, Forbes; Twitter: @Forbes
6. Domain-specific data sources are necessary for most businesses. “Choosing NLP software that understands the terminology used in your industry is absolutely essential. If your shortlisted NLP software can’t understand common industry terms, it may cause errors in data analysis, resulting in inaccurate insights. If you operate in industries such as legal, medical, and finance that use complex industry-specific jargons, you should shortlist a product that contains deep industry/domain knowledge so your organization can fully reap the benefits of NLP.” - Natural Language Processing (NLP) Software Buyers Guide, Capterra; Twitter: @Capterra
7. Designing your own NLP models is possible but requires high-level expertise. “Open-source libraries [...] are free, versatile, and enable you to completely modify your NLP applications. They are, however, designed for developers, and as a result, they are very complicated to comprehend. If you want to construct open-source natural language processing tools, you will require prior machine-learning skills. Fortunately, since the majority of them are community-driven frameworks, you can expect to get a great deal of assistance.
“If you want to design your natural language processing models using open-source libraries, you'll need time to set up infrastructure from scratch and money to hire developers if you don't already have a team of specialists on staff.” - What are the Top Trending Natural Language Processing Tools in 2023?, Aegis Softtech; Twitter: @AegisSoftTech
8. Consider the solution’s ability to analyze context. “Computers traditionally require humans to ‘speak’ to them in a programming language that is precise, unambiguous and highly structured -- or through a limited number of clearly enunciated voice commands. Human speech, however, is not always precise; it is often ambiguous and the linguistic structure can depend on many complex variables, including slang, regional dialects and social context.” - Ben Lutkevich and Ed Burns, Natural Language Processing (NLP), Tech Target; Twitter: @TTBusinessTech
9. Consider integration, customization, scalability, and other factors. “I’m inclined to say that it’s a more viable approach to develop an NLP app. First, the software will provide a tailored experience. Second, you’ll have complete control over software and data. Of course, you won’t be coding from scratch, given all available open-source and commercial MLP libraries.
However, if you still find an NLP solution catering to your particular needs, ensure that you’ll have some flexibility:
- Can you add custom add-ons?
- Will the software scale at a whim?
- Who owns the data?
- Are there enough security checks in place?
- Can you integrate the solution with other software/vendors?”
- Konstantin Kalinin, How to Develop a Natural Language Processing App, Topflight Apps; Twitter: @toplightapps1
10. Choose a solution that offers advanced conversation analysis. “Conversational speech is squarely in the capabilities of NLP, but users are human, and, like all humans, they sometimes omit words, make spelling and punctuation mistakes, use colloquial terms, or use slang or ‘conversational’ language. Today’s NLP solutions can take these challenges into account and deliver search results that are accurate, relevant, and valuable for the customer.” - The Guide to Natural Language Processing, AndPlus; Twitter: @AndPlus
11. Small and mid-size businesses often can use single-purpose solutions to meet immediate business needs, but large enterprises require advanced NLP software features. “These buyers want a feature-rich data analysis platform with advanced features such as speech-to-text analysis and predictive analytics. The platform should also offer integration with a wide range of enterprise tools and support functionalities such as the ability to analyze and derive insights from customer journeys across multiple channels such as social media or email.” - Find the best Natural Language Processing (NLP) Software, Software Advice; Twitter: @SoftwareAdvice
12. Know the difference between supervised and unsupervised analysis methods. “ Sentiment Analysis informs us whether our data is correlated with an optimistic or pessimistic outlook. Although there are various techniques of producing sentiment analysis, typical use cases include defining the emotion conveyed in a statement or collection of sentences in order to achieve a general interpretation of the customers’ mood. In marketing, this can be helpful in understanding how people respond to various types of communication.
“Sentiment analysis can be conducted using both supervised and unsupervised methods. Naive Bayes is the most common supervised model used for sentiment analysis. Besides Naive Bayes, other machine learning methods like the random forest or gradient boosting can also be used. Unsupervised approaches, also known as lexicon-based strategies involve a corpus of words with their related feeling and polarity. The sentence’s sentiment score is determined using the phrase’s polarity.” - Anmol Preet Singh, Natural Language Processing: Definition and Technique Types, AiThority; Twitter: @AiThority
13. Consider the pros and cons of cloud-based and in-house solutions. “The choice between cloud and in-house is a decision that would be influenced by what features the business needs. If your business needs a highly capable chatbot with custom dialogue facility and security, you might want to develop your own engine. In some cases, in-house NLP engines do offer matured natural language understanding components, cloud providers are not as strong in dialogue management.
“When you want to hold a context-based chat, in-house chatbots would be more useful. For example, say customers need to ask a specific question such as: ‘Show me the details of the orange product.’ Chatbots may not retrieve what ‘orange product’ was from the database. In this scenario, context is very important and moreover, these scenarios are dynamic. In this case, cloud-based providers may not be suitable as they are built for generic target market use cases. Here is where going for in-house NLP would add value.” - Which NLP Engine to Use In Chatbot Development, V-Soft Consulting; Twitter: @VSoftConsulting
14. Ensure that the NLP software you choose can ingest custom data. “Apple has specialized NLP and an ‘army’ of software/NLP engineers, who can take on the challenge and build such a chatbot (Siri). Apple’s team built Siri from the ground up with the single objective of answering Apple-related questions for Apple’s customers.
“On the other hand, a smaller Fintech company does not have such a specialization and has to rely on third party NLP products, which they will use to create their chatbot. The company needs to select a product that can ingest the company’s data, understand various processes and output a chatbot that can answer questions specific to the company.” - George Karagiannis, Natural Language Processing – Practical Applications of NLP for Product Teams, Department of Product; Twitter: @Deptofproduct
15. Consider NLP software solutions that offer in-context learning. “On many benchmark NLP benchmarks, in-context learning is competitive with models trained with much more labeled data and is state-of-the-art on LAMBADA (commonsense sentence completion) and TriviaQA (question answering). Perhaps even more exciting is the array of applications that in-context learning has enabled people to spin up in just a few hours, including writing code from natural language descriptions, helping with app design mockups, and generalizing spreadsheet functions.
“In-context learning allows users to quickly build models for a new use case without worrying about fine-tuning and storing new parameters for each task. It typically requires very few training examples to get a prototype working, and the natural language interface is intuitive even for non-experts.” - Sang Michael Xie and Sewon Min, How does in-context learning work? A framework for understanding the differences from traditional supervised learning, The Stanford AI Lab Blog; Twitter: @StanfordAILab
16. Transfer learning enables NLP software to work with real-time data. “Datasets are expanding at breakneck speed; new data is being generated every second, and old information is updated in real time. It’s difficult to retrain models frequently from scratch for new data. So here, transfer learning comes to the rescue.
“Transfer learning is a technique in which a pretrained model that has already been trained on different but somehow similar problems is used. The benefits of transfer learning are:
- Helps solve real-world complex problems
- Saves time, effort, and machine memory
- Handles Big Data more easily
“There are many transfer learning models available, including: Embedding from Language Models (ELMo), Transformer, and Bidirectional Encoder Representations from Transformers (BERT). BERT is the latest pretrained model that can be used for various NLP tasks.” - Overcoming Common Challenges in Natural Language Processing, Sisense; Twitter: @Sisense
17. Consider the volume of training text, the level of detail needed from extraction, and quality and performance requirements. “Before jumping into selecting technologies and doing proofs of concept, it’s important to ground any NLP experimentation with a defined scope and success criteria. Be sure to understand the volume of training texts or documents, the level of detail required from the extraction, the types of information required, the overall quality required from the extraction, and the performance required in processing new texts. The best experiments are when business value can be delivered on modest requirements and more sophistication added through an agile, iterative process.” - Isaac Sacolick, Get started with natural language processing, InfoWorld; Twitter: @InfoWorld
18. Choose NLP software that obtains data in a way that suits your business’s needs. “Here, “data scraping” (or “information scraping”) refers to how your NLP software acquires data for analysis. While the source(s) here will depend on your business’s specific applications and needs for NLP, the software you ultimately choose should be able to work alongside it.
“For example, if you’re a financial analytics firm, you would probably want your NLP software to constantly take in data from the stock market or major financial publications. On the other hand, if you’re like many businesses using NLP for personal assistance or customer support, you would probably want your NLP software to interface with whichever mode of communication(s) you intend to use (e.g. voice, chat, etc.).” - Best Natural Language Processing software of 2022, Think Big Analytics
How to get the most from your NLP software
19. Don’t treat language like data. “Though data is the basis of analytics that can help businesses understand their customers better, when it comes to natural language processing, this is not the case. Each data string needs to be treated individually instead of clubbing it together. Like each voice is unique, each request is unique, so should its results. Computational statistics and pattern recognition is not the best approach when it comes to artificial intelligence that uses natural language understanding. Simply transforming language into data and not understanding the context is a flaw that will need to be addressed.” - Leveraging natural language processing and NLP tools to their fullest, CallMiner; Twitter: @CallMiner
20. Recognize the shortcomings of NLP. “Natural language is hard. Even as human, sometimes we find difficulties in interpreting each other’s sentences or correcting our text typos. NLP faces different challenges which make its applications prone to error and failure.
“Some of the major challenges of NLP include:
- Phrase ambiguity
- Slang or street language
- Domain-specific language
- Bias in training data
“However, these challenges are being tackled today with advancements in NLU, deep learning and community training data which create a window for algorithms to observe real-life text and speech and learn from it.” - Cem Dilmegani, Complete Guide to NLP in 2023: How It Works & Top Use Cases, AIMultiple; Twitter: @AIMultiple
21. Keep humans in the loop. “An NLP-centric workforce builds workflows that leverage the best of humans combined with automation and AI to give you the ‘superpowers’ you need to bring products and services to market fast.
“Many data annotation tools have an automation feature that uses AI to pre-label a dataset; this is a remarkable development that will save you time and money.
“Although automation and AI processes can label large portions of NLP data, there’s still human work to be done. You can’t eliminate the need for humans with the expertise to make subjective decisions, examine edge cases, and accurately label complex, nuanced NLP data.” - The Ultimate Guide to Natural Language Processing (NLP), CloudFactory; Twitter: @CloudFactory
22. Sentiment analysis is best-suited for text with a subjective context. “The key aspect of sentiment analysis is to analyze a body of text for understanding the opinion expressed by it. Typically, we quantify this sentiment with a positive or negative value, called polarity. The overall sentiment is often inferred as positive, neutral or negative from the sign of the polarity score.
“Usually, sentiment analysis works best on text that has a subjective context than on text with only an objective context. Objective text usually depicts some normal statements or facts without expressing any emotion, feelings, or mood. Subjective text contains text that is usually expressed by a human having typical moods, emotions, and feelings. Sentiment analysis is widely used, especially as a part of social media analysis for any domain, be it a business, a recent movie, or a product launch, to understand its reception by the people and what they think of it based on their opinions or, you guessed it, sentiment!” - Dipanjan (DJ) Sarkar, A Practitioner's Guide to Natural Language Processing (Part I) — Processing & Understanding Text, Towards Data Science; Twitter: @TDataScience
23. Recognize that NLP software may not accurately interpret ambiguity. “In natural language, there is rarely a single sentence that can be interpreted without ambiguity. Ambiguity in natural language processing refers to sentences and phrases interpreted in two or more ways. Ambiguous sentences are hard to read and have multiple interpretations, which means that natural language processing may be challenging because it cannot make sense out of these sentences. Word sense disambiguation is a process of deciphering the sentence meaning.” - Wojciech Marusarz, The 2022 Definitive Guide to Natural Language Processing (NLP), Nexocode; Twitter: @nexocode_com
24. Use NLP software to transform marketing and customer service. “NLP has been around for a while, with its first major use in the 1950s, when it was used in a cryptography application, and it has now grown into something that can be used for a bunch of different things, like sentiment analysis: understanding how people feel about your brand. This is useful for companies, brands, and e-commerce stores that want to use data to improve their business. Customer service: the more you know about your customers, the more you can help them. This is why a lot of companies and stores use NLP to help them with customer service. For example, a company can use NLP to give better support to their customers, by getting to know their preferences, likes, and dislikes, and how to talk to them in a way they will understand.” - Rijul Singh Malik, How to Get the Most Out of NLP: A Blog on what NLP can do for you, Towards AI
25. Train models using open datasets and your company’s datasets. “Many sectors, and even divisions within your organization, use highly specialized vocabularies. Through a combination of your data assets and open datasets, train a model for the needs of specific sectors or divisions. Think of finance. You do not want a model specialized in finance. You want a model customized for commercial banking, or for capital markets. And data is critical, but now it is unlabeled data, and the more the better. Specialized models like this can unlock untold value for your firm.” - Ross Gruetzemacher, The Power of Natural Language Processing, Harvard Business Review; Twitter: @HarvardBiz