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Best Natural Language Processing software of 2020

#1

Rulai

47%
#2

Amazon Comprehend

46%
#3

TextBlob

46%
#4

Google Cloud Natural Language

45%
#5

IBM Watson Tone Analyzer

45%
#6

IBM Watson Knowledge Studio

45%
#7

Microsoft Bing Autosuggest API

44%
#8

Gensim

43%
#9

Nuance Vocalizer

43%
#10

NaturalReader

42%

Related to Natural Language Processing Software

Natural Language Processing Software Trends

Natural language processing (NLP) software provides you with the tools for analyzing human languages. Unlike voice recognition software, however, NLP software is capable of interpreting both written and spoken languages, making it useful for an extremely wide range of applications.

One of the most common applications of NLP software is in translation, where it’s already proved effective at extracting subtle nuance and context between different languages. NLP has also found widespread use in other areas, particularly in applications of deep learning and home automation.

Despite these hyper-specific examples, however, even the most generalized businesses can benefit from adopting NLP software. As NLP continues to improve, businesses are already using it to automatically interpret communications such as emails, phone calls, and even technical documentation.

Why use NLP software?

Language is the most abundant type of data in the world, but it’s one of the hardest to interpret.

Think of any conversation you’ve ever had; how do you interpret the meaning of someone else’s words? What parts of their sentences indicate their intentions or desires? Could you use these parts to somehow predict their behavior?

Analyzing language this way is the entire idea behind NLP software. Where linguists have studied the structure and meaning of languages for centuries, NLP software takes the analysis a step further, using advanced algorithms to extract “meaning” from human languages in the same way humans do.

But why use NLP software when humans can already extract meaning on their own?

NLP software excels as an automation solution, being able to analyze large quantities of data with high speed and accuracy. Where a human would need time to both listen to and think about a sentence, NLP software can perform the same analysis instantly—and perhaps pick up on hidden meanings and nuances in the process.

This capability has become especially useful for home automation software. Thanks to NLP software, home automation devices can usually carry out complex spoken requests, such as “lower the temperature a little and then play my favorite song.” This capability is at a level beyond basic voice recognition.

Where earlier forms of voice recognition might be pre-programmed to listen to a predefined “list” of words, NLP software can analyze almost any sentence, regardless of the speaker’s native language, accent, or verbal nuances. Some NLP software can even analyze the speaker’s speech patterns to determine more information about the speaker!

In essence, where voice recognition software identifies spoken words, NLP software identifies both spoken words and their meanings, all based on the context in which the words were spoken.

However, the capability of NLP software extends far beyond the spoken word; human language takes on many forms, after all! Whether it’s speech, writing, or even symbolic gestures, NLP software can extract deep, hidden meanings from most human languages.

This ability has made NLP software indispensable for many unique analytics applications—some of which might surprise you.

Who uses NLP software?

NLP software is finding increasing use in applications involving any form of language analysis. While some of these applications are a bit obvious, the ability to quickly and accurately extract subtle meaning from speech and text has led to many unique applications.

AI, Machine Learning, and Deep Learning Applications

At its core, NLP software is a specific application of machine learning software where language serves as unlabeled data. The data (read: language) is analyzed through one of many different algorithms depending on the desired outcome.

Generally, however, the goal of both NLP and machine learning is the same: extracting or “learning” the meaning behind a given “sample” of language (read: data). Where extracting insights is often the goal for various applications of AI and machine learning, NLP software can be especially useful for language-based inputs.

Common applications of NLP in AI and machine learning include home automation software, data science, and business intelligence. While we’ll cover these in greater detail in the coming sections, applications of NLP for deep learning deserves its own discussion.

Deep learning is a subcategory of machine learning where algorithms and models attempt to learn on their own. Where many conventional machine learning applications rely on “trained” or “labeled” datasets to help guide their decision making (supervised learning), deep learning applications perform unsupervised learning on unlabeled, untrained datasets.

Here, NLP software plays an important role in the deep learning process. Where some particularly novel applications of deep learning have taken to reading books on their own, NLP allows them to extract meaningful insights from such texts—or, in other words, learn in a similar way that humans do while reading a book.

Most NLP applications are much less abstract but still employ the same principles which allow for deep learning. The following applications will highlight some of these more practical uses.

Home Automation and Personal Assistant Software

Home automation, such as Amazon’s Alexa and Google Home, are among the most recognizable examples of NLP software at work. Here, lean (but robust) algorithms analyze users’ speech to understand requests. In most cases, the device can usually make sense of the request and carry it out as the user(s) intended.

This ability is impressive, especially when compared to earlier forms of voice recognition and NLP. Where the predecessors of these devices might have maintained a pre-defined databank of words and definitions, NLP software can take on more “abstract” inputs.

For example, suppose you gave a home automation device a request such as “play that song again and add bread to my grocery list.” This may seem relatively benign, but there’s a lot at work here: The NLP software in your home automation device essentially “listens” to your request, picking through it for bits of meaning and making its best judgment about what you meant (and it’s usually right!).

The impressive part of this is what it wouldn’t matter exactly how you said the request or whether it was you or your friend who said it; in any case, as long as the command is relatively understandable (no mumbling!) and spoken in a known human language, there’s a good chance that the NLP software would be able to carry out the request.

As expected, this capability has also allowed NLP to extend into the realm of personal assistants. Where someone would have previously told their human assistant to cancel a meeting or make a reservation, NLP can now do so with instantaneous speed and efficiency.

Data Scientists

NLP software can have multiple uses in data science. While data science is often dominated by more conventional forms of machine learning, some areas – particularly those treating human language as raw data – rely on NLP to generate useful insights.

Perhaps the most common use of NLP software for data scientists is for formatting raw data. While languages are indeed data, it often needs to be converted into more “usable” formats (for example, speech to text) before it can be analyzed. NLP exceeds in this application, allowing data scientists to convert voice recordings or live speech into manipulatable data.

Software Engineers and Programmers

As both NLP and machine learning finds its way into various software applications, many software engineers and programmers are now tasked with implementing NLP. As some companies offer products based entirely around NLP (such as home automation), many software engineers are now also working exclusively with NLP software.

Thankfully, most NLP software packages offer programmers prebuilt suites of NLP tools, allowing engineers to avoid the hard work of building NLP algorithms themselves. This availability and ease of access have made NLP easy to implement for many software engineers and programmers.

Healthcare Analytics and Research

NLP has become an increasingly valuable tool for the healthcare industry, having found use in diagnosing diseases and analyzing patient data.

In the doctor’s office, NLP gives doctors the ability to automatically detect underlying diseases based on the patient’s speech patterns. This ability becomes especially effective after repeat visits, where some NLP software integrated with electronic medical records (EMRs) can analyze changes in speech patterns to determine corresponding changes in patient health over time.

NLP is also beginning to play a role in psychological therapies, where “therapy bots” are beginning to offer an alternative to scheduled, in-person therapy sessions.

Financial Analytics

Where financial analysis software often relies on raw data to identify trends, NLP software uses a much more familiar source—the news.

Instead of relying solely on numbers, some NLP software can analyze news broadcasts and publications to generate a conceptual understanding of emerging market trends right as they’re occurring. Here, NLP allows financial software to analyze markets and financial data in a very human way.

Spam Filters, Business Intelligence, and Everything Else

Since language forms the foundation of every business, there’s room for NLP software just about anywhere.

One common application of NLP for businesses is detecting spam emails. While some applications of machine learning already do this with the help of trained datasets, NLP can “read” emails much as humans can. As a result, NLP software has become a more effective solution to spam filtering than many conventional technologies.

Another application of NLP is in business intelligence. While this particular application is very multi-faceted, most implementations function similarly to the financial analytics application discussed earlier. Here, NLP software can analyze the news to determine market trends in real-time. While this ability has obvious benefits for financial analysis, it can also benefit business intelligence in many ways. Imagine being instantly notified about a product recall or new law related to your business offering?

These are only a handful of applications that can benefit from NLP software. Again, as language is everywhere, any business in any industry can benefit from NLP. Regardless of the application, however, all NLP software should share the same common features.

Features

NLP software should perform data/information scraping in a way that best suits your business. 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.).

NLP software usually includes a robust syntax evaluation. Here, syntax evaluation refers to how exactly the software analyzes samples of language (such as text). In any case, language is usually converted into data for manipulation, allowing the NLP software to perform syntax evaluation.

A common form of syntax evaluation found in NLP software is lemmatization, a process where a given word is converted into its “root” dictionary word (or “lemma”). For example, lemmatizing the word “better” would return its lemma, “good.” This process is crucial for analyzing sentences based on their core meanings.

Similar to lemmatization is stemming, where inflected words are reduced to their immediate roots (such as reducing “baking” to “bake”). Other forms of syntax evaluation include grammar induction, parsing, and word segmentation, among others.

In any case, any solid NLP software will be capable of performing a robust syntax evaluation. Of course, this is only half the battle; as we’ll see shortly, syntax is nothing without semantics.

NLP software must also include at least one major type of semantics evaluation. Where syntax is the “structure” of words, semantics are the “meaning.” After performing syntax evaluation, NLP software can use the gathered data to perform semantics evaluation to extract practical meaning from language.

Sentiment analysis is a common type of syntax evaluation in NLP, which attempts to assign a “polarity” to certain words and sentences. Other examples include machine translation (for translating human languages), optical character representation (reading printed text), and question-answer—just to name a few.

While syntax evaluation capabilities should be nearly universal for NLP software, there’s a little more room for flexibility when it comes to semantics evaluation. Here, the right type of semantics evaluation for you will be the one most applicable for your desired application (such as question answering for chatbots).  

NLP software should generate a valid output based on your business needs. This point should probably go without saying, but it still bears importance! No matter how effective your NLP software is at scraping data and analyzing languages, it won’t be of any use to you if it doesn’t generate some form of useful output.

While the output will vary depending on your specific needs, many NLP software packages are fairly generalized (with some exceptions). As a result, you may need a software engineer to help tailor certain NLP outputs to your business needs.

FAQ

Q: What is natural language processing?

A: Natural language processing (commonly abbreviated “NLP”) is a type of machine learning specialized for analyzing human languages. Unlike more conventional forms of machine learning, NLP utilizes advanced forms of unsupervised learning to effectively “read” or “listen” in a way similar to humans.

NLP software works to provide businesses with this ability. Being able to automatically process human languages, businesses can optimize customer support, business intelligence, and nearly every part of their organization.

Q: Is NLP machine learning?

A: NLP is only a subcategory of machine learning, which is itself a much broader subject. As discussed in the previous question, NLP works somewhat differently from conventional machine learning; where machine learning often works with trained datasets, NLP applications attempt to extract meaning on their own.

Ultimately, NLP and machine learning work toward the same goal (finding insight and patterns in data), but the type of data is different. Typically, machine learning works with “formal” datasets (pixel grids, numerical data, etc.) while NLP deals almost exclusively with human languages.

Q: What are some common NLP algorithms?

A: NLP uses several algorithms to deconstruct language. These algorithms (referred to here as forms of “evaluation”) work primarily with the syntax and semantics – or structure and meaning, respectively – of human language.

On the syntax side, common NLP algorithms include those for lemmatizing words and relating them to their basic forms. Other syntax algorithms include those responsible for breaking apart sentences, inducing grammatical structures, and segmenting words. While different NLP software packages might approach these tasks differently, they (ideally) accomplish similar tasks.

On the semantics side, NLP algorithms are a bit more varied between software packages. Here, depending on the application, certain NLP software might focus on algorithms such as sentiment analysis. In any case, most software packages will come complete with enough algorithms to make sense of nearly any natural language. However, it’s still important to note that some algorithms will be better suited to some applications.

Q: What is a language processing hierarchy, and how does it relate to NLP?

A: The language processing hierarchy, developed by educator Gail Richards in 2011, is a holistic model of language processing in early childhood education. While not directly related to natural language processing in the software sense, its fundamental structure can help software engineers and scientists engineer NLP more effectively. This point will become especially true as applications of machine learning and AI come closer to emulating aspects of human cognition.

In general, however, it’s highly unlikely you will need to worry about the language processing hierarchy in the educational sense of the term.

Q: Do I need an NLP certification?

A: You do not need an NLP certification to use NLP software. “NLP certification” typically refers to certification in “neuro-linguistic programming,” a long-discredited psychological therapy. In our case, NLP refers to “natural language processing” and has no relation to the pseudoscientific therapy sharing the same acronym.

Q: Does NLP software require an NLP practitioner?

A: Some applications of NLP software may require an NLP practitioner (or a particularly knowledgeable software engineer) to implement effectively. While it’s unlikely that you’ll need to keep an NLP practitioner in the long run, they are often essential for getting things to run smoothly out of the gate.

For example, suppose that you’ve chosen a more generalized NLP software package. It may be capable of scraping and analyzing the data you want, and it may even generate a useful result, but the output might be a little bit “messier” than you’re looking for. This isn’t very useful!

A good NLP practitioner will be able to take this output and tailor it to your specific needs. They may also be able to help smooth out certain NLP processes and optimize your applications.

Q: What is the MTK NLP service? Is natural language processing related to MTK NLP?

A: MTK NLP, short for “MediaTek Natural Language Processing,” is an NLP service native to Android phones. While somewhat notorious for taking full location access to some Android phones, Android’s MTK NLP service is not necessarily representative of NLP as a whole. However, the MTK NLP service does allow for certain NLP applications in Android phones, specifically Google Assistant and Google Lookup—two common applications of NLP software!

Note that if you have had problems with MTK NLP on your Android phone, you will not also have problems with NLP software in your business environment.

Q: What are some examples of natural language processing with Python? How can I implement Python NLP?

A: Python is a popular programming language for those working in both data science and machine learning. As such, many NLP software packages are written in Python, and the language is becoming increasingly favored by many businesses for its understandable syntax and mathematical capabilities.

Some examples of Python-based NLP platforms include the Natural Language Toolkit (NLTK) and spaCy.

Q: Should I take the Stanford NLP course?

A: While non-Stanford students cannot enroll in Stanford’s NLP course, the university has provided many lectures for free access online. While most businesses implementing NLP will not need to take a formal course in the subject, software engineers or anyone working directly with NLP software might benefit from reviewing the course material.

Recap

NLP software can be a powerful tool for effectively analyzing human languages. Since language is everywhere, your business is bound to benefit from using NLP software in some way.