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Executive Interview: Perry Lea, Book Author, Entrepreneur, Director of Architecture: Microsoft

New Applications Foreseen from Combination of AI and Powerful Edge Devices Perry Lea is a 30-year veteran technologist. He spent over 20 years at Hewlett-Packard as a chief architect and distinguished technologist of the LaserJet business. He then led a team at Micron as a technologist and strategic director, working on emerging compute using in-memory […]

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“IoT and Edge Computing” for Architects by Perry Lea provides an effective guide to how advances in AI, sensors, processors and networks have put us on the verge of a new era. (Published by Packt.)

New Applications Foreseen from Combination of AI and Powerful Edge Devices

Perry Lea is a 30-year veteran technologist. He spent over 20 years at Hewlett-Packard as a chief architect and distinguished technologist of the LaserJet business. He then led a team at Micron as a technologist and strategic director, working on emerging compute using in-memory processing for machine learning and computer vision. Perry’s leadership extended to Cradlepoint, where he pivoted the company into 5G and the Internet of Things (IoT). Soon afterwards, he co-founded Rumble, an industry leader in edge/IoT products. He was also a principal architect for Microsoft’s Xbox and xCloud and today is a director of architecture for Microsoft. Perry has degrees in computer science and computer engineering, and an EngrD in electrical engineering from Columbia University. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and a senior member/ distinguished speaker of the Association for Computing Machinery (ACM). He holds 50 patents, with 30 pending. After recently publishing a new book, “IoT and Edge Computing for Architects,”  he took a few minutes to speak with AI Trends Editor John P. Desmond about his work.

Perry Lea, book author, startup founder, Director of Architecture: Microsoft

AI Trends: Thank you Perry for talking to us today, just after the release of your new book, very timely. So what would you say is the best way to define the Internet of Things?

Perry Lea: Well I like to define it as the ability to connect the previously unconnectable world. Thirty years ago we didn’t have the technology, or the genuine interest in connecting inanimate objects or unconnectable things and do it pervasively. So, what’s happened in the last 30 years is we’ve ridden on Moore’s Law, Dennard scaling [Ed. Note: As transistors get smaller, their power density stays constant.], Nielsen’s Law [Ed. Note: Users’ internet bandwidth grows by 50 percent per year.], the big hierarchy of computer science and computer engineering laws that have driven the industry. That has now stretched down into IoT devices connecting things, people, animals, vegetables and minerals. And now you have the ability to do that at scale, and at a cost point that you can do interesting things.

And people are coming up with very creative and interesting and sometimes business-savvy IoT devices, and other times they’re more of hype and fads, or have a business model that just won’t succeed. So the book is about IoT and edge computing, which kind of talks about that, but from an enterprise commercial scale, industrial scale.

Sounds good. Can you say what is the business case for IoT and edge computing? Will it save money?

Well, it can save money, or it can make money. There are business cases where it’s positive, and there are also business cases where it simply doesn’t make sense. I’ll briefly talk about those later.

I tend to think of IoT, first and foremost, as the insurance of things, not the Internet of Things. And I have personal experience with this too in saving my own home from flood damage. In a lot of cases, the devices are monitoring, they’re watching, they’re trying to find a problem before it becomes exacerbated for the customer or the client.

And you think of things like connected homes. Well, a lot of connected homes are about monitoring the state of the home, the temperature, whether the furnace is working  or the air conditioning. Or whether an alarm or a flood alert went off.  Those are for insurance, and you extend that to retail and enterprise.

In the enterprise and industrial setting, sensors look at things like predictive maintenance. They can analyze classical devices on a factory floor that haven’t been touched in 40 years. You can monitor them to find out when a spindle or a bearing will wear out. That saves money and time. It is the insurance of things.

In other cases, a lot of information is accumulated and you can apply more learning to it, some adaptive and predictive analytics, to do some cool things. So, in a lot of cases I think the IoT is about the insurance of things, remotely monitoring devices, and edge computing is a little different. I put that in a different category.

What does AI bring to the table for IoT?

Well you now have the ability to collect a lot of information, and as you are able to connect an entire herd of livestock, for example, you can start monitoring the behavior and health of animals. You can start accumulating data on that herd. And this is an example where you bring AI to this exorbitant amount of data, and you are able to train models to find for example, sickly animals, or find underweight animals, or animals that are carriers of a disease, before that disease spreads, say in a feedlot, or in a farm.

So, AI can absorb this accumulation of data and that’s good, because you’re accumulating quite a bit of data. IoT is only useful by the number of nodes that you’re connecting; that’s Nielsen’s Law. And the more you connect, the more valuable your network is. And the more connected devices, the more data that you can produce, and AI machine learning and deep learning are applications there.

That comes with a problem though, because as you’re connecting more and more devices, you start plaguing this network with bottlenecks from the sheer amount of data you’re moving over wires or into the cloud. The trend is to actually run inference on the edge, and keep the data in the cloud. That’s the typical model that’s used right now. There are some exceptions, but that helps in curbing the amount of data that you have to transport, and also reduces your latency.

These are all really important from a system point of view, because you don’t just sprinkle a little AI on a bunch of IoT devices, and hope to garner some deeply hidden messages for your business. You actually have to build the setup from a system point of view; otherwise it becomes too costly and impractical. I’ve seen a lot of projects fail on their proof of concept because of that.

I know that sensors are important for IoT. What are some of the trends in sensor technology?

Sensors aren’t new. There is micro-electronic sensing, there is sound, infrared, lidar, optical- and vision-based sensors. Anything that can receive an input and generate a signal or generate a binary value, is a sensor. You’re moving data from the analog world to the digital world, and that’s how they’ve been working for the last 60 years.

Trends in sensing technology are that more intelligence is being placed on the sensors. So again, riding Moore’s law, you can do more at the sensor level at the far edge. You can rule out bad data, you can run machine learning or deep learning models on sensors, or on vision based sensors, and do inference there and rule out false negatives, or false positives and only extract data that’s meaningful. It has everything to do with reducing the amount of data that you’re pushing over the network, and doing more work closer to where the data is being generated.

You have the ability now to sense everything around sound and vibration, including looking into other parts of the spectrum. Some sensors are looking at Wi-Fi signals, and using them to snoop through walls and to find out where people are in emergency situations or in a catastrophe. And some sensors are doing very interesting things with lidar and radar-based technologies, encountering objects and looking at objects. So the advances in sensor electronics have greatly been enhanced in the last 10 years, riding on all the IoT devices that are building that industry.

What are the major trends in edge computing?

Edge computing has been around for a long time. Back 30 years ago, I called it embedded systems. And again, with Moore’s law, you’re able to actually push very powerful computing, very close to IoT devices, and sensors, and aggregate data and perform what had been previously required in a cloud, on the edge.

We are seeing a shift from 40 years of traditional IT in a corporate setting. If you’re in a corporation and you have an IT department, you have policies and security controls and software updates, and IT is basically managing the information and the appliances that work with the data.

Edge computing could be in a completely remote location in the middle of nowhere, completely unmanned, and not conveniently placed in a data center at a corporate premise. So they’re unsecure, they’re unreliable, and there’s no staff there, but they’re building edge systems that are capable of integrating very seamlessly to corporate IT infrastructure. So an IT manager can manage an edge computing device, and an OT [operational technology] user can actually use the device and peer into the environmental control, or some appliance.

That’s a major step, because that’s taken the last 60 years of IT policy learning and formalized it into edge computers. Many interesting things are going on with edge computing. Ambient computing is pushing these edge computers everywhere, but making them unobtrusive, making them part of just the environment, having them communicate with each other and in controlling an entire environment and experience holistically.

So you don’t see an edge computer in a little black box with a monitor connected to it. They’re embedded in walls, they’re embedded in lights, in switches, in the infrastructure of a residence, or a place of commerce, or an industry, and they’re just ubiquitous.

Another interesting trend I’m excited about is synthetic sensing, which requires the performance you get with high-performance edge boxes, but it takes all of these different data streams coming from the sensors connected to it, which could include heat sensors, vibration sensors, electromagnetic sensing and whatnot. It could be normalizing all that data from hundreds of different sensors, and using deep learning models to actually extract information that would not be perceivable with just one single sensor.

So you can look at an entire environment in the home, for example, and understand if someone left a burner on without having an actual sensor attached to a stove or a burner. Those are some exciting trends in sensors.

What impact do you think 5G will have on IoT and edge computing?

Well, 5G has provisions for IoT devices. One of the claims is that they can have greatly improved density. Another claim is that they can have a million IoT devices, streaming data within a square kilometer. And they also have provisions — one I think is absolutely necessary — which is lower latency. So a device can actually send data with sub one millisecond latency to an edge device, or the cloud, or whatnot. That starts to enable at least some degree of real time control over IoT devices.

I think it is actually more meaningful for edge computing. Because in edge computing there’s a technology called  MEC [mobile edge computing]. 5G is constructed as basically a virtualized system. Everything is a virtual extension running off of hypervisors through the entire 5G stack. That allows for companies to start building edge appliances very close to the customers.

So for example, in a streaming service, 5G could be used with edge blades or edge hardware in a carrier substation, or cell tower, or within a local premise that the carrier might be using. And they would lease space, and lease time on their network for 5G services.

So, a streaming service like Netflix, or video gaming and streaming could develop MEC hardware that would be installed in a data center, very close to the user, reducing latency and resolving similar network traffic. So 5G is really built for edge computing, and edge hardware appliances. That’s kind of a big shift from where 4G LTE [Long-Term Evolution] was. You probably won’t see any use cases like this popping up in the next year. But I think within the next five years, edge computing and 5G will be pervasive.

What is fog computing? Why do we need that?

I tend to call fog computing a subset of edge computing. Essentially you need a computer at the edge somewhere, and fog computing is an extension of the cloud. We see a lot of hype and buzzwords around things like this, like mist computing and whatnot, but essentially it’s an extension of the APIs, and the services that you would see in the cloud.

So if you’re developing a traditional SaaS application in the cloud, you will need a DevOps team, programmers very familiar with developing applications and deploying them at scale on the cloud. Fog extends that, so these nodes running at the edge are essentially extensions of the cloud.

And from a developer standpoint, they don’t really need to know where the software is residing, or how it’s auto-scaled, or how it’s deployed on the cloud or the edge, or maybe a hybrid of both. And it’s really a practicality for modern programmers who are very cloud focused and SaaS focused, who will face very little learning curve to start developing edge-based software. So a classic example would be something like Amazon IoT Greengrass, or Azure IoT.

These are extensions that allow you to deploy things in the cloud, or on the edge, or you can have an extension of both. From a programmatic point of view, it’s easy to manage, easy to code for and easy to scale. But it does have that cloud-based component to it that you have to consider. And not all applications that people are developing for IoT and edge really need the fog. Some edge- based devices can be completely autonomous by design.

In the security area, what are the risks that hackers can get control of, or they can spy on an IoT system? Can the systems be made secure?

Well, there have been issues with some home appliances. In one instance, some secure information was available to employees of the organization that was building the appliance. There have been breaches, there have been denial of service attacks; The biggest attack in the world, the Mirai attack [August 2016] was based on essentially IoT devices. It was a massive denial of service, that used these appliances to basically attack the internet, and shut down specific websites.

That will happen again. Like I said earlier, a lot of these edge devices are now catching up to 60 years of IT security management and policies. They have been traditional embedded systems, and embedded systems have been secure through obscurity for a very long time.

Well now they’re connected, and they’re running Linux stacks. They’re sitting there with open ports. They haven’t had all the hardening and the maturity that a traditional server in a data center has, and now edge systems are the servers. So there is a risk, and a lot of the risks can be mitigated and controlled through basically common practices, common security provisions that you would employ in your home or in your corporation, but you would also apply them on the edge. Security professionals have many measures to choose from. With IoT, some degree of security maturity needs to be built into the system.

Are there any consortiums that are involved with IoT that you would care to highlight?

Well, I’m a big proponent of IEEE and the Eclipse Foundation, eclipse.org. Many organizations come and go, but IEEE is a very good organization. They have a subchapter for IoT that is worth looking at. Personal area networks are very prevalent now; Bluetooth SIG is a very good organization to join to help keep up to date on new standards. Another one is the OASIS group, the organization that controls the communication from edge devices to the cloud.

They do a lot more, but one prevalent communication standard is called MQTT. It’s an open standard, and is by far the most prevalent edge-to-cloud communication system that exists. Others I would call out are the Object Management Group, and the task forces they have. They do a lot of work with UML and organizations like that.

On the connectivity side, you could throw in the ITU and 3GPP [The 3rd Generation Partnership Project, umbrella term for a number of standards groups developing protocols for mobile communications.] as organizations to look out for telecommunications senders, especially 5G. Also, the Wi-Fi Alliance is working on Wi-Fi post 802.11ac and 802.11ax, with some really exciting development happening now. Many organizations are out there; those are the standouts in the various segments of IoT.

What are some of the use cases for IoT and edge computing that in your view illustrate what the technology is delivering today, and maybe what the potential is for the future?

When you look at successful IoT deployments, things that are driven at scale and where there’s high volume growth and interest, one obvious area that continues to grow is telematics, as in fleet telematics and mobile telematics.

This is where you have a vehicle or a ship or carrier, and you’re monitoring the status of the cargo, and of the delivery and trying to optimize it. You try to ensure, for example in dairy production where you have a lot of milk being generated in a constant volume, that you know where you’re moving it. It has a shelf life; you can only keep it in a tank for so long. It has to be cooled or kept within a range of temperatures; you’re under the gun.

And so you’re monitoring the fleet, you’re monitoring the passage of this material, the movement of this material, and you’re monitoring the health of the material, the temperature range that it falls in, so you can place it at the right processing center at the right time to have this constant stream, this flow of milk production be managed.

The same is true, and in other areas in telematics, for example, in municipalities, monitoring your fleet of snowplows, or your fleet of garbage trucks and garbage collection, to understand where they are, and how they’re operating.  In snow removal, you don’t want to send multiple plows to the same street and not use them optimally.

So you’re trying to extract and optimize your problem, and you can only optimize a problem when you can measure it. So IoT allows you to measure what has been previously unmeasurable. Other use cases around residential include personal assistants, security systems, irrigation systems. Many people put a high value on being able to monitor their home remotely. That saved me from a flooding situation I was able to respond to before there was additional damage. As more advanced processing is moving close to the devices through edge computing, it opens up many new opportunities.

You mentioned in the beginning that some things are not appropriate for IoT and edge computing. You want to mention maybe some of those things?

I’ve seen some devices that are, I couldn’t understand the business behind, many of them consumer brands. One was an IoT connected hairbrush that monitors how you brush your hair. I was thinking, what data are you gathering from that? How does it optimize a problem? That didn’t make any sense to me.

Another device monitors how often you’re drinking water from an internet-connected bottle of water. Maybe there is a business case for some medical situations to monitor patients or maybe in home care, but this was being marketed into the hiking and weekend enthusiast community. I couldn’t see justifying paying five times the price for a water bottle, where you could measure and monitor how many times you’ve lifted it to drink.

So there’s a lot of hype and fads that kind of distract from the reality. That’s where you have to put some blinders on, and not embed intelligence into every device, just because you can.

If there are any students or early career professionals out there reading this, who and they want to get involved with IoT and edge computing, what should they study, or how do you recommend they go up the learning curve?

Well, when I got involved with this, when I wrote the first book, and the IoT and Edge Computing for Architects third book, I mean, what became absolutely clear was if you’re taking data, and you’re building an IoT device, and you’re putting something at the edge, and you want to do something intelligent and beneficial with that data, with that device and with whatever it’s connected to, you get involved with many domains of engineering and technology when building a holistic system.

I would also advise that if you have a very narrow or myopic view, if you’re very siloed, such as being focused exclusively in SaaS development, I would extend that to other domains of engineering as well. In this call we’re talking about everything from sensor physics, to electronics, to energy harvesting, to embedded system design, the things that are traditionally in a computer engineering or electrical engineering realm.

When you start moving into embedding operating systems, IT appliances, IT policies and hardening your edge device, now you’re starting to develop security skills.

We didn’t really talk about all this, but the way communication happens with wireless and wired IoT devices is a huge consideration, and has many ramifications on the overall cost or even whether you’re going to succeed in your project.  So you need an understanding of telecommunications from a signal point of view, an understanding of the Bluetooth stack, and the ability to decide whether to use 5G everywhere. That probably isn’t a good idea. Understanding all of that helps.  I briefly talked about MQTT and other protocols, and all the nuances of those protocols in a pub-sub type of protocol. [Publish/subscribe messaging is used in serverless and microservices architectures.] You need to know the security issues there.

When you start building a network, you can have North, South, East and West components, edge computers talking to each other. You need to decide what to do with the data, maybe it marshals up to the cloud. So understanding SaaS skills, or platforms as a service (PaaS), or how auto-scaling works, or basically cloud dynamics are all very useful skills.

So you have all this data you’ve accumulated in the cloud, and now you want to do something meaningful for customers. They are going to want visibility into that data. So you need to understand UI and user experience dynamics, where they can see a lot of data and garner information easily just looking at some kind of chart.

Then you want to go a step further and try to do something intelligent with the data. Now you need machine learning skills, and data science skills. Maybe not necessarily deep learning in all applications, but you might use a Bayesian type machine learning model, or you might use a random decision forest or just a decision tree. Embedding that expert knowledge into a system that can take all this data and give you something like predictive analytics, or predictive maintenance is invaluable.

Understanding that whole stack is really helpful. I think it’s good to be an expert in one area, but it’s really quite helpful to understand that whole spectrum of technologies.

That’s a lot to know. It seems having one major, like computer science, would be too narrow.

When I wrote the book, it was for architects, and it’s for anyone. If you’re a SaaS developer, you can start by looking at cloud-based chapters of the book and then extend outward. You might be deep in one area, and broad in a lot of areas. With some exposure, at least you can start talking the same vernacular. You can start making decisions based on where you put your training for a deep learning system. What types of hardware do you need to run a good, realtime inference on the edge? How much power is that going to need? What if you don’t have a good reliable power source, how is that going to work?

IoT devices have many nuances. If you’re doing fleet telematics and you drive under a tunnel, and lose your signal, what happens? Do you cache data on the edge? Do you process locally?  You’re no longer completely digital, you’re dealing with the analog world, and doing it on a massive scale. Stuff goes wrong all over the place.

Those are all the questions I had. Is there anything you would like to add or emphasize?

We live in an interesting time, right now, fighting the coronavirus, and people are learning that doing things remotely is where the future’s going. Look at autonomous driving, remote work and highly-automated systems. This is a growing area. When you wipe away all the hype and the surrounding fads, including claims that will have a trillion IoT devices by 2025 or 2030, it is a double-digit growth area. So it is where to go, and there’s a lot of room for improvement.

Learn more about Perry Lea.

Source: https://www.aitrends.com/executive-interview/executive-interview-perry-lea-book-author-entrepreneur-director-of-architecture-microsoft/

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How to Get the Best Start at Sports Betting

If you are looking into getting into sports betting, then you might be hesitant about how to start, and the whole idea of it can be quite daunting. There are many techniques to get the best possible start at sports betting and, in this article, we will have a look at some of the best […]

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If you are looking into getting into sports betting, then you might be hesitant about how to start, and the whole idea of it can be quite daunting. There are many techniques to get the best possible start at sports betting and, in this article, we will have a look at some of the best tips for that.

Mental preparation

This sounds a bit pretentious, but it is very important to understand some things about betting before starting so you can not only avoid nasty surprises but also avoid losing too much money. Firstly, you need to know that, in the beginning, you will not be good at betting. It is through experience and learning from your mistakes that you will get better. It is imperative that you do not convince yourself that you are good at betting, especially if you win some early bets, because I can guarantee it will have been luck – and false confidence is not your friend. 

It is likely that you will lose some money at first, but this is to be expected. Almost any hobby that you are interested in will cost you some money so, instead, look at it as an investment. However, do not invest ridiculous amounts; rather, wait until you are confident in your betting ability to start placing larger stakes. 

Set up different accounts

This is the best way to start with sports betting, as the welcome offers will offset a lot of the risk. These offers are designed to be profitable to entice you into betting with the bookie, but it is completely legal to just profit from the welcome offer and not bet with the bookie again. 

If you do this with the most bookies, as you can, you are minimising the risk involved with your betting and maximising possible returns, so it really is a no-brainer.

As well as this clear advantage, different betting companies offer different promotions. Ladbrokes offer a boost every day, for example, where you can choose your bet and boost it a little bit, and the Parimatch betting website chooses a bet for big events and doubles the odds. 

If you are making sure you stay aware of the best offers across these platforms, then you will be able to use the most lucrative ones and, as such, you will be giving yourself the best chance of making money. The house always wins, as they say, but if you use this tip, you are skewing the odds back in your favour. 

Remember, the house wins because of gamblers that do not put in the effort and do not bet smart. Avoid those mistakes and you will massively increase your chances of making money.

Tipsters

On Twitter, especially, but also other social media platforms, there are tipsters who offer their bets for free. It is not so much the bets themselves that you are interested in, but rather why they are betting on this. It is important that you find tipsters who know what they are doing, though, because there are a lot of tipsters who are essentially scamming their customers. It is quite easy to find legitimate tipsters because they are not afraid to show their mistakes. 

Once you have found good tipsters, then you need to understand the reasoning behind their bets. When you have done that, you can start placing these bets yourself, and they will likely be of better value since some tipsters influence the betting markets considerably. You can also follow their bets as they are likely to be sensible bets, although this does not necessarily translate to success.

Source: https://1reddrop.com/2020/10/20/how-to-get-the-best-start-at-sports-betting/?utm_source=rss&utm_medium=rss&utm_campaign=how-to-get-the-best-start-at-sports-betting

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Natural Language Processing in Production: 27 Fast Text Pre-Processing Methods

Estimates state that 70%–85% of the world’s data is text (unstructured data) [1]. New deep learning language models (transformers) have caused explosive growth in industry applications [5,6,11]. This blog is not an article introducing you to Natural Language Processing. Instead, it assumes you are familiar with noise reduction and normalization of text. It covers text preprocessing up […]

The post Natural Language Processing in Production: 27 Fast Text Pre-Processing Methods appeared first on TOPBOTS.

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text pre-processing

Estimates state that 70%–85% of the world’s data is text (unstructured data) [1]. New deep learning language models (transformers) have caused explosive growth in industry applications [5,6,11].

This blog is not an article introducing you to Natural Language Processing. Instead, it assumes you are familiar with noise reduction and normalization of text. It covers text preprocessing up to producing tokens and lemmas from the text.

We stop at feeding the sequence of tokens into a Natural Language model.

The feeding of that sequence of tokens into a Natural Language model to accomplish a specific model task is not covered here.

In production-grade Natural Language Processing (NLP), what is covered in this blog is that fast text pre-processing (noise cleaning and normalization) is critical.

  1. I discuss packages we use for production-level NLP;
  2. I detail the production-level NLP preprocessing text tasks with python code and packages;
  3. Finally. I report benchmarks for NLP text pre-processing tasks;

Dividing NLP Processing into Two Steps

We segment NLP into two major steps (for the convenience of this article):

  1. Text pre-processing into tokens. We clean (noise removal) and then normalize the text. The goal is to transform the text into a corpus that any NLP model can use. A goal is rarely achieved until the introduction of the transformer [2].
  2. A corpus is an input (text preprocessed into a sequence of tokens) into NLP models for training or prediction.

The rest of this article is devoted to noise removal text and normalization of text into tokens/lemmas (Step 1: text pre-processing). Noise removal deletes or transforms things in the text that degrade the NLP task model. It is usually an NLP task-dependent. For example, e-mail may or may not be removed if it is a text classification task or a text redaction task. We’ll cover replacement and removal of the noise.

Normalization of the corpus is transforming the text into a common form. The most frequent example is normalization by transforming all characters to lowercase. In follow-on blogs, we will cover different deep learning language models and Transformers (Steps 2-n) fed by the corpus token/lemma stream.

NLP Text Pre-Processing Package Factoids

There are many NLP packages available. We use spaCy [2], textacy [4], Hugging Face transformers [5], and regex [7] in most of our NLP production applications. The following are some of the “factoids” we used in our decision process.

Note: The following “factoids” may be biased. That is why we refer to them as “factoids.”

NLTK [3]

  • NLTK is a string processing library. All the tools take strings as input and return strings or lists of strings as output [3].
  • NLTK is a good choice if you want to explore different NLP with a corpus whose length is less than a million words.
  • NLTK is a bad choice if you want to go into production with your NLP application [3].

Regex

The use of regex is pervasive throughout our text-preprocessing code. Regex is a fast string processor. Regex, in various forms, has been around for over 50 years. Regex support is part of the standard library of Java and Python, and is built into the syntax of others, including Perl and ECMAScript (JavaScript);

spaCy [2]

  • spaCy is a moderate choice if you want to research different NLP models with a corpus whose length is greater than a million words.
  • If you use a selection from spaCy [3], Hugging Face [5], fast.ai [13], and GPT-3 [6], then you are performing SOTA (state-of-the-art) research of different NLP models (my opinion at the time of writing this blog).
  • spaCy is a good choice if you want to go into production with your NLP application.
  • spaCy is an NLP library implemented both in Python and Cython. Because of the Cython, parts of spaCy are faster than if implemented in Python [3];
  • spacy is the fastest package, we know of, for NLP operations;
  • spacy is available for operating systems MS Windows, macOS, and Ubuntu [3];
  • spaCy runs natively on Nvidia GPUs [3];
  • explosion/spaCy has 16,900 stars on Github (7/22/2020);
  • spaCy has 138 public repository implementations on GitHub;
  • spaCy comes with pre-trained statistical models and word vectors;
  • spaCy transforms text into document objects, vocabulary objects, word- token objects, and other useful objects resulting from parsing the text ;
  • Doc class has several useful attributes and methods. Significantly, you can create new operations on these objects as well as extend a class with new attributes (adding to the spaCy pipeline);
  • spaCy features tokenization for 50+ languages;

Do you find this in-depth technical education about NLP applications to be useful? Subscribe below to be updated when we release new relevant content.

Creating long_s Practice Text String

We create long_, a long string that has extra whitespace, emoji, email addresses, $ symbols, HTML tags, punctuation, and other text that may or may not be noise for the downstream NLP task and/or model.

MULPIPIER = int(3.8e3)
text_l = 300 %time long_s = ':( 😻 😈 #google +1 608-444-0000 08-444-0004 608-444-00003 ext. 508 '
long_s += ' 888 eihtg DoD Fee https://medium.com/ #hash ## Document Title</title> '
long_s += ':( cat- \n nip'
long_s += ' immed- \n natedly <html><h2>2nd levelheading</h2></html> . , '
long_s += '# [email protected] [email protected] can\'t Be a ckunk. $4 $123,456 won\'t seven '
long_s +=' $Shine $$beighty?$ '
long_s *= MULPIPIER
print('size: {:g} {}'.format(len(long_s),long_s[:text_l]))

output =>

CPU times: user 3 µs, sys: 1 µs, total: 4 µs
Wall time: 8.11 µs
size: 1.159e+06 :( 😻 😈 #google +1 608-444-0000 08-444-0004 608-444-00003 ext. 508 888 eihtg DoD Fee https://medium.com/ #hash ## Document Title</title> :( cat- nip immed- natedly <html><h2>2nd levelheading</h2></html> . , # [email protected] [email protected] can't Be a ckunk. $4 $123,456 won't seven $Shine $$beigh

A string, long_s of 1.159 million characters is created in 8.11 µs.

Python String Corpus Pre-processing Step and Benchmarks

All benchmarks are run within a Docker container on MacOS Version 14.0 (14.0).

Model Name: Mac Pro
Processor Name: 12-Core Intel Xeon E5
Processor Speed: 2.7 GHz
Total Number of Cores: 24
L2 Cache (per Core): 256 KB
L3 Cache: 30 MB
Hyper-Threading Technology: Enabled Memory: 64 GB

Note: Corpus/text pre-processing is dependent on the end-point NLP analysis task. Sentiment Analysis requires different corpus/text pre-processing steps than document redaction. The corpus/text pre-processing steps given here are for a range of NLP analysis tasks. Usually. a subset of the given corpus/text pre-processing steps is needed for each NLP task. Also, some of required corpus/text pre-processing steps may not be given here.

1. NLP text preprocessing: Replace Twitter Hash Tags

from textacy.preprocessing.replace import replace_hashtags
%time text = replace_hashtags(long_s,replace_with= 'HASH')
print('size: {:g} {}'.format(len(text),text[:text_l])))

output =>

CPU times: user 223 ms, sys: 66 µs, total: 223 ms
Wall time: 223 ms
size: 1.159e+06 :
( 😻 😈 _HASH_ +1 608-444-0000 08-444-0004 608-444-00003 ext. 508 888 eihtg DoD Fee https://medium.com/ _HASH_ ## Document Title</title> :( cat- nip immed- natedly <html><h2>2nd levelheading</h2></html> . , # [email protected] [email protected] can't Be a ckunk. $4 $123,456 won't seven $Shine $$beigh

Notice that #google and #hash are swapped with_HASH_,and ##and _# are untouched. A million characters were processed in 200 ms. Fast enough for a big corpus of a billion characters (example: web server log).

2. NLP text preprocessing: Remove Twitter Hash Tags

from textacy.preprocessing.replace import replace_hashtags
%time text = replace_hashtags(long_s,replace_with= '')
print('size: {:g} {}'.format(len(text),text[:text_l]))

output =>

CPU times: user 219 ms, sys: 0 ns, total: 219 ms
Wall time: 220 ms
size: 1.1134e+06 :( 😻 😈 +1 608-444-0000 08-444-0004 608-444-00003 ext. 508 888 eihtg DoD Fee https://medium.com/ ## Document Title</title> :( cat- nip immed- natedly <html><h2>2nd levelheading</h2></html> . , # [email protected] [email protected] can't Be a ckunk. $4 $123,456 won't seven $Shine $$beighty?$

Notice that #google and #hash are removed and ##,and _# are untouched. A million characters were processed in 200 ms.

3. NLP text preprocessing: Replace Phone Numbers

from textacy.preprocessing.replace import replace_phone_numbers
%time text = replace_phone_numbers(long_s,replace_with= 'PHONE')
print('size: {:g} {}'.format(len(text),text[:text_l]))

output =>

CPU times: user 384 ms, sys: 1.59 ms, total: 386 ms
Wall time: 383 ms
size: 1.0792e+06
:( 😻 😈 PHONE 08-PHONE 608-444-00003 ext. 508 888 eihtg

Notice phone number 08-444-0004 and 608-444-00003 ext. 508 were not transformed.

4. NLP text preprocessing: Replace Phone Numbers – better

RE_PHONE_NUMBER: Pattern = re.compile( # core components of a phone number r"(?:^|(?<=[^\w)]))(\+?1[ .-]?)?(\(?\d{2,3}\)?[ .-]?)?(\d{2,3}[ .-]?\d{2,5})" # extensions, etc. r"(\s?(?:ext\.?|[#x-])\s?\d{2,6})?(?:$|(?=\W))", flags=re.UNICODE | re.IGNORECASE) text = RE_PHONE_NUMBER.sub('_PHoNE_', long_s)
print('size: {:g} {}'.format(len(text),text[:text_l]))

output =>

CPU times: user 353 ms, sys: 0 ns, total: 353 ms
Wall time: 350 ms
size: 1.0108e+06 :( 😻 😈 _PHoNE_ _PHoNE_ _PHoNE_ 888 eihtg DoD Fee https://medium.com/ ## Document Title</title> :( cat- nip immed- natedly <html><h2>2nd levelheading</h2></html> . , # [email protected] [email protected] can't Be a ckunk. $4 $123,456 won't seven $Shine $$beighty?$

Notice phone number 08-444-0004 and 608-444-00003 ext. 508 were transformed. A million characters were processed in 350 ms.

5. NLP text preprocessing: Remove Phone Numbers

Using the improved RE_PHONE_NUMBER pattern, we put '' in for ‘PHoNE' to remove phone numbers from the corpus.

text = RE_PHONE_NUMBER.sub('', long_s)
print('size: {:g} {}'.format(len(text),text[:text_l]))

output =>

CPU times: user 353 ms, sys: 459 µs, total: 353 ms
Wall time: 351 ms
size: 931000 :( 😻 😈 888 eihtg DoD Fee https://medium.com/ ## Document Title</title> :( cat- nip immed- natedly <html><h2>2nd levelheading</h2></html> . , # [email protected] [email protected] can't Be a ckunk. $4 $123,456 won't seven $Shine $$beighty?$

A million characters were processed in 375 ms.

6. NLP text preprocessing: Removing HTML metadata

I admit removing HTML metadata is my favorite. Not because I like the task, but because I screen-scrape frequently. There is a lot of useful data that resides on an IBM mainframe, VAX-780 (huh?), or whatever terminal-emulation that results in an HTML-based report.

These techniques of web scraping of reports generate text that has HTML tags. HTML tags are considered noise typically as they are parts of the text with little or no value in the follow-on NLP task.

Remember, we created a test string (long_s) a little over million characters long with some HTML tags. We remove the HTML tags using BeautifulSoup.

from bs4 import BeautifulSoup
%time long_s = BeautifulSoup(long_s,'html.parser').get_text()
print('size: {:g} {}'.format(len(long_s),long_s[:text_l])))

output =>

CPU times: user 954 ms, sys: 17.7 ms, total: 971 ms
Wall time: 971 ms
size: 817000 :( 😻 😈 888 eihtg DoD Fee https://medium.com/ ## Document Title :( cat- nip immed- natedly 2nd levelheading 

The result is that BeautifulSoup is able to remove over 7,000 HTML tags in a million character corpus in one second. Scaling linearly, a billion character corpus, about 200 million word, or approxiately 2000 books, would require about 200 seconds.

The rate for HTML tag removal byBeautifulSoup is about 0. 1 second per book. An acceptable rate for our production requirements.

I only benchmark BeautifulSoup. If you know of a competitive alternative method, please let me know.

Note: The compute times you get may be multiples of time longer or shorter if you are using the cloud or Spark.

7. NLP text preprocessing: Replace currency symbol

The currency symbols “[$¢£¤¥ƒ֏؋৲৳૱௹฿៛ℳ元円圆圓﷼\u20A0-\u20C0] “ are replaced with _CUR_using the textacy package:

%time textr = textacy.preprocessing.replace.replace_currency_symbols(long_s)
print('size: {:g} {}'.format(len(textr),textr[:text_l]))

output =>

CPU times: user 31.2 ms, sys: 1.67 ms, total: 32.9 ms
Wall time: 33.7 ms
size: 908200 :( 😻 😈 888 eihtg DoD Fee https://medium.com/ ## Document Title :( cat- nip immed- natedly 2nd levelheading . , # [email protected] [email protected] can't Be a ckunk. _CUR_4 _CUR_123,456 won't seven _CUR_Shine _CUR__CUR_beighty?_CUR_

Note: The option textacy replace_<something> enables you to specify the replacement text. _CUR_ is the default substitution text for replace_currency_symbols.

You may have the currency symbol $ in your text. In this case you can use a regex:

%time text = re.sub('\$', '_DOL_', long_s)
print('size: {:g} {}'.format(len(text),text[:250]))

output =>

CPU times: user 8.06 ms, sys: 0 ns, total: 8.06 ms
Wall time: 8.25 ms
size: 1.3262e+06 :( 😻 😈 #google +1 608-444-0000 08-444-0004 608-444-00003 ext. 508 888 eihtg DoD Fee https://medium.com/ #hash ## <html><title>Document Title</title></html> :( cat- nip immed- natedly <html><h2>2nd levelheading</h2></html> . , # [email protected] [email protected] can't Be a ckunk. _DOL_4 _DOL_123,456 won't seven _DOL_Shine _DOL__DOL_beighty?_DOL_ :

Note: All symbol $ in your text will be removed. Don’t use if you have LaTex or any text where multiple symbol $ are used.

8. NLP text preprocessing: Replace URL String

from textacy.preprocessing.replace import replace_urls
%time text = replace_urls(long_s,replace_with= '_URL_')
print('size: {:g} {}'.format(len(text),text[:text_l]))

output =>

CPU times: user 649 ms, sys: 112 µs, total: 649 ms
Wall time: 646 ms
size: 763800
:( 😻 😈 888 eihtg DoD Fee _URL_ ## Document Title :(

9. NLP text preprocessing: Remove URL String

from textacy.preprocessing.replace import replace_urls
%time text = replace_urls(long_s,replace_with= '')
print('size: {:g} {}'.format(len(text),text[:text_l]))

output =>

CPU times: user 633 ms, sys: 1.35 ms, total: 635 ms
Wall time: 630 ms
size: 744800
:( 😻 😈 888 eihtg DoD Fee ## Document Title :(

The rate for URL replace or removal is about 4,000 URLs per 1 million characters per second. Fast enough for 10 books in a corpus.

10. NLP text preprocessing: Replace E-mail string

%time text = textacy.preprocessing.replace.replace_emails(long_s)
print('size: {:g} {}'.format(len(text),text[:text_l]))

output =>

CPU times: user 406 ms, sys: 125 µs, total: 406 ms
Wall time: 402 ms
size: 725800
:( 😻 😈 888 eihtg DoD Fee ## Document Title :( cat-
nip immed-
natedly 2nd levelheading . , # _EMAIL_ _EMAIL_ can't Be a ckunk. $4 $123,456 won't seven $Shine $$beighty?$

The rate for email reference replace is about 8,000 emails per 1.7 million characters per second. Fast enough for 17 books in a corpus.

11. NLP text pre-processing: Remove E-mail string

from textacy.preprocessing.replace import replace_emails

%time text = textacy.preprocessing.replace.replace_emails(long_s,replace_with= '')
print('size: {:g} {}'.format(len(text),text[:text_l]))

output =>

CPU times: user 413 ms, sys: 1.68 ms, total: 415 ms
Wall time: 412 ms
size: 672600 :( 😻 😈 888 eihtg DoD Fee ## Document Title :( cat-
nip immed-
natedly 2nd levelheading . , # can't Be a ckunk. $4 $123,456 won't seven $Shine $$beighty?$

The rate for email reference removal is about 8,000 emails per 1.1 million characters per second. Fast enough for 11 books in a corpus.

12. NLP text preprocessing: normalize_hyphenated_words

from textacy.preprocessing.normalize import normalize_hyphenated_words
%time long_s = normalize_hyphenated_words(long_s)
print('size: {:g} {}'.format(len(long_s),long_s[:text_l])))

output =>

CPU times: user 186 ms, sys: 4.58 ms, total: 191 ms
Wall time: 190 ms
size: 642200 :
( 😻 😈 888 eihtg DoD Fee ## Document Title :( catnip immednatedly

Approximately 8,000 hyphenated-words, cat — nip and immed- iately (mispelled) were corrected in a corpus of 640,000 characters in 190 ms or abouut 3 million per second.

13. NLP text preprocessing: Convert all characters to lower case

### - **all characters to lower case;**
%time long_s = long_s.lower()
print('size: {:g} {}'.format(len(long_s),long_s[:text_l]))

output =>

CPU times: user 4.82 ms, sys: 953 µs, total: 5.77 ms
Wall time: 5.97 ms
size: 642200
:( 😻 😈 888 eihtg dod fee ## document title :( catnip immednatedly 2nd levelheading . , # can't be a ckunk. $4 $123,456 won't seven $shine $$beighty?$

I only benchmark the .lower Python function. The rate for lower case transformation by.lower() of a Python string of a million characters is about 6 ms. A rate that far exceeds our production rate requirements.

14. NLP text preprocessing: Whitespace Removal

%time text = re.sub(' +', ' ', long_s)
print('size: {:g} {}'.format(len(text),text[:text_l]))

output =>

CPU times: user 44.9 ms, sys: 2.89 ms, total: 47.8 ms
Wall time: 47.8 ms
size: 570000
:( 😻 😈 888 eihtg dod fee ## document title :( catnip immednatedly 2nd levelheading . , # can't be a ckunk. $4 $123,456 won't seven $shine $$beighty?$

The rate is about 0.1 seconds for 1 million characters.

15. NLP text preprocessing: Whitespace Removal (slower)

from textacy.preprocessing.normalize import normalize_whitespace

%time text= normalize_whitespace(long_s)
print('size: {:g} {}'.format(len(text),text[:text_l]))

output =>

CPU times: user 199 ms, sys: 3.06 ms, total: 203 ms
Wall time: 201 ms
size: 569999
:( 😻 😈 888 eihtg dod fee ## document title :( catnip immednatedly 2nd levelheading . , # can't be a ckunk. $4 $123,456 won't seven $shine $$beighty?$

normalize_whitespce is 5x slower but more general. For safety in production, we use normalize_whitespce.To date, we do not think we had any problems with faster regex.

16. NLP text preprocessing: Remove Punctuation

from textacy.preprocessing.remove import remove_punctuation

%time text = remove_punctuation(long_s, marks=',.#$?')
print('size: {:g} {}'.format(len(text),text[:text_l]))

output =>

CPU times: user 34.5 ms, sys: 4.82 ms, total: 39.3 ms
Wall time: 39.3 ms
size: 558599
:( 😻 😈 888 eihtg dod fee document title :( catnip immednatedly 2nd levelheading can't be a ckunk 4 123 456 won't seven shine beighty

spaCy

Creating the spaCy pipeline and Doc

In order to text pre-process with spaCy, we transform the text into a corpus Doc object. We can then use the sequence of word tokens objects of which a Doc object consists. Each token consists of attributes (discussed above) that we use later in this article to pre-process the corpus.

Our text pre-processing end goal (usually) is to produce tokens that feed into our NLP models.

  • spaCy reverses the stream of pre-processing text and then transforming text into tokens. spaCy creates a Doc of tokens. You then pre-process the tokens by their attributes.

The result is that parsing text into a Doc object is where the majority of computation lies. As we will see, pre-processing the sequence of tokens by their attributes is fast.

Adding emoji cleaning in the spaCy pipeline

import en_core_web_lg
nlp = en_core_web_lg.load() do = nlp.disable_pipes(["tagger", "parser"])
%time emoji = Emoji(nlp)
nlp.max_length = len(long_s) + 10
%time nlp.add_pipe(emoji, first=True)
%time long_s_doc = nlp(long_s)
print('size: {:g} {}'.format(len(long_s_doc),long_s_doc[:text_l]))

output =>

CPU times: user 303 ms, sys: 22.6 ms, total: 326 ms
Wall time: 326 ms
CPU times: user 23 µs, sys: 0 ns, total: 23 µs
Wall time: 26.7 µs
CPU times: user 7.22 s, sys: 1.89 s, total: 9.11 s
Wall time: 9.12 s
size: 129199
:( 😻 😈 888 eihtg dod fee document title :( catnip immednatedly 2nd levelheading can't be a ckunk 4 123 456 won't seven shine beighty

Creating the token sequence required at 14,000 tokens per second. We will quite a speedup when we use NVIDIA gpu.

nlp.pipe_names output => ['emoji', 'ner']

Note: The tokenizer is a “special” component and isn’t part of the regular pipeline. It also doesn’t show up in nlp.pipe_names. The reason is that there can only be one tokenizer, and while all other pipeline components take a Doc and return it, the tokenizer takes a string of text and turns it into a Doc. You can still customize the tokenizer. You can either create your own Tokenizer class from scratch, or even replace it with an entirely custom function.

spaCy Token Attributes for Doc Token Preprocessing

As we saw earlier, spaCy provides convenience methods for many other pre-processing tasks. It turns — for example, to remove stop words you can reference the .is_stop attribute.

dir(token[0]) output=> 'ancestors', 'check_flag', 'children', 'cluster', 'conjuncts', 'dep', 'dep_', 'doc', 'ent_id', 'ent_id_', 'ent_iob', 'ent_iob_', 'ent_kb_id', 'ent_kb_id_', 'ent_type', 'ent_type_', 'get_extension', 'has_extension', 'has_vector', 'head', 'i', 'idx', 'is_alpha', 'is_ancestor', 'is_ascii', 'is_bracket', 'is_currency', 'is_digit', 'is_left_punct', 'is_lower', 'is_oov', 'is_punct', 'is_quote', 'is_right_punct', 'is_sent_end', 'is_sent_start', 'is_space', 'is_stop', 'is_title', 'is_upper', 'lang', 'lang_', 'left_edge', 'lefts', 'lemma', 'lemma_', 'lex_id', 'like_email', 'like_num', 'like_url', 'lower', 'lower_', 'morph', 'n_lefts', 'n_rights', 'nbor', 'norm', 'norm_', 'orth', 'orth_', 'pos', 'pos_', 'prefix', 'prefix_', 'prob', 'rank', 'remove_extension', 'right_edge', 'rights', 'sent', 'sent_start', 'sentiment', 'set_extension', 'shape', 'shape_', 'similarity', 'string', 'subtree', 'suffix', 'suffix_', 'tag', 'tag_', 'tensor', 'text', 'text_with_ws', 'vector', 'vector_norm', 'vocab', 'whitespace_']

Attributes added by emoji and new.

dir(long_s_doc[0]._) output => ['emoji_desc', 'get', 'has', 'is_emoji', 'set', 'trf_alignment', 'trf_all_attentions', 'trf_all_hidden_states', 'trf_d_all_attentions', 'trf_d_all_hidden_states', 'trf_d_last_hidden_state', 'trf_d_pooler_output', 'trf_end', 'trf_last_hidden_state', 'trf_pooler_output', 'trf_separator', 'trf_start', 'trf_word_pieces', 'trf_word_pieces_'

I show spaCy performing preprocessing that results in a Python string corpus. The corpus is used to create a new sequence of spaCy tokens (Doc).

There is a faster way to accomplish spaCy preprocessing with spaCy pipeline extensions [2], which I show in an upcoming blog.

17. EMOJI Sentiment Score

EMOJI Sentiment Score is not a text preprocessor in the classic sense.

However, we find that emoji almost always is the dominating text in a document.

For example, two similar phrases from legal notes e-mail with opposite sentiment.

The client was challenging. :( The client was difficult. :)

We calcuate only emoji when present in a note or e-mail.

%time scl = [EMOJI_TO_SENTIMENT_VALUE[token.text] for token in long_s_doc if (token.text in EMOJI_TO_SENTIMENT_VALUE)]
len(scl), sum(scl), sum(scl)/len(scl)

output =>

CPU times: user 179 ms, sys: 0 ns, total: 179 ms
Wall time: 178 ms
(15200, 1090.7019922523152, 0.07175671001659968)

The sentiment was 0.07 (neutral) for 0.5 million character “note” with 15,200 emojis and emojicons in 178 ms. A fast sentiment analysis calculation!

18. NLP text preprocessing: Removing emoji

You can remove emoji using spaCy pipeline add-on

%time long_s_doc_no_emojicon = [token for token in long_s_doc if token._.is_emoji == False]
print('size: {:g} {}'.format(len(long_s_doc_no_emojicon),long_s_doc_no_emojicon[:int(text_l/5)]))

output =>

CPU times: user 837 ms, sys: 4.98 ms, total: 842 ms
Wall time: 841 ms
size: 121599
[:(, 888, eihtg, dod, fee, , document, title, :(, catnip, immednatedly, 2nd, levelheading, , ca, n't, be, a, ckunk, , 4, , 123, 456, wo, n't, seven, , shine, , beighty, , :(, 888, eihtg, dod, fee, , document, title, :(, catnip, immednatedly, 2nd, levelheading, , ca, n't, be, a, ckunk, , 4, , 123, 456, wo, n't, seven, , shine, , beighty, , :(, 888, eihtg, dod, fee, ]

The emoji spacy pipeline addition detected the emojicons, 😻 😈, but missed :) and :(.

19. NLP text pre-processing: Removing emoji (better)

We developed EMOJI_TO_PHRASEto detect the emojicons, 😻 😈, and emoji, such as :) and :(. and removed them [8,9].

%time text = [token.text if (token.text in EMOJI_TO_PHRASE) == False \
else '' for token in long_s_doc]
%time long_s = ' '.join(text)
print('size: {:g} {}'.format(len(long_s),long_s[:text_l]))

output =>

CPU times: user 242 ms, sys: 3.76 ms, total: 245 ms
Wall time: 245 ms
CPU times: user 3.37 ms, sys: 73 µs, total: 3.45 ms
Wall time: 3.46 ms
size: 569997
888 eihtg dod fee document title catnip immednatedly 2nd levelheading ca n't be a ckunk 4 123 456 wo n't seven shine beighty 888 eihtg dod fee document title catnip immednatedly 2nd levelheading ca n't be a ckunk 4 123 456 wo n't seven shine beighty 888 eihtg dod fee document title catnip imm

20. NLP text pre-processing: Replace emojis with a phrase

We can translate emojicon into a natural language phrase.

%time text = [token.text if token._.is_emoji == False else token._.emoji_desc for token in long_s_doc]
%time long_s = ' '.join(text)
print('size: {:g} {}'.format(len(long_s),long_s[:250]))

output =>

CPU times: user 1.07 s, sys: 7.54 ms, total: 1.07 s
Wall time: 1.07 s
CPU times: user 3.78 ms, sys: 0 ns, total: 3.78 ms
Wall time: 3.79 ms
size: 794197
:( smiling cat face with heart-eyes smiling face with horns 888 eihtg dod fee document title :( catnip immednatedly 2nd levelheading ca n't be a ckunk 4 123 456 wo n't seven shine beighty

The emoji spaCy pipeline addition detected the emojicons, 😻 😈, but missed :) and :(.

21. NLP text pre-processing: Replace emojis with a phrase (better)

We can translate emojicons into a natural language phrase.

%time text = [token.text if (token.text in EMOJI_TO_PHRASE) == False \
else EMOJI_TO_PHRASE[token.text] for token in long_s_doc]
%time long_s = ' '.join(text)
print('size: {:g} {}'.format(len(long_s),long_s[:text_l]))

output =>

CPU times: user 251 ms, sys: 5.57 ms, total: 256 ms
Wall time: 255 ms
CPU times: user 3.54 ms, sys: 91 µs, total: 3.63 ms
Wall time: 3.64 ms
size: 904397
FROWNING FACE SMILING CAT FACE WITH HEART-SHAPED EYES SMILING FACE WITH HORNS 888 eihtg dod fee document title FROWNING FACE catnip immednatedly 2nd levelheading ca n't be a ckunk 4 123 456 wo n't seven shine beighty FROWNING FAC

Again. EMOJI_TO_PHRASE detected the emojicons, 😻 😈, and emoji, such as :) and :(. and substituted a phrase.

22. NLP text preprocessing: Correct Spelling

We will use symspell for spelling correction [14].

SymSpell, based on the Symmetric Delete spelling correction algorithm, just took 0.000033 seconds (edit distance 2) and 0.000180 seconds (edit distance 3) on an old MacBook Pro [14].

%time sym_spell_setup() 
%time tk = [check_spelling(token.text) for token in long_s_doc[0:99999]]
%time long_s = ' '.join(tk)
print('size: {:g} {}'.format(len(long_s),long_s[:250]))

output =>

CPU times: user 5.22 s, sys: 132 ms, total: 5.35 s
Wall time: 5.36 s
CPU times: user 25 s, sys: 12.9 ms, total: 25 s
Wall time: 25.1 s
CPU times: user 3.37 ms, sys: 42 µs, total: 3.41 ms
Wall time: 3.42 ms
size: 528259 FROWNING FACE SMILING CAT FACE WITH HEART a SHAPED EYES SMILING FACE WITH HORNS 888 eight do fee document title FROWNING FACE catnip immediately and levelheading a not be a chunk a of 123 456 to not seven of shine of eighty

Spell correction was accomplished for immednatedly, ckunk and beight. Correcting mis-spelled words is our largest computation. It required 30 seconds for 0.8 million characters.

23. NLP text preprocessing: Replacing Currency Symbol (spaCy)

%time token = [token.text if token.is_currency == False else '_CUR_' for token in long_s_doc]
%time long_s = ' '.join(token)
print('size: {:g} {}'.format(len(long_s),long_s[:text_l]))aa

Note: spacy removes all punctuation including :) emoji and emoticon. You can protect the emoticon with:

%time long_s_doc = [token for token in long_s_doc if token.is_punct == False or token._.is_emoji == True]
print('size: {:g} {}'.format(len(long_s_doc),long_s_doc[:50]))

However, replace_currency_symbols and regex ignore context and replace any currency symbol. You may have multiple use of $ in your text and thus can not ignore context. In this case you can use spaCy.

%time tk = [token.text if token.is_currency == False else '_CUR_' for token in long_s_doc]
%time long_s = ' '.join(tk)
print('size: {:g} {}'.format(len(long_s),long_s[:250]))

output =>

CPU times: user 366 ms, sys: 13.9 ms, total: 380 ms
Wall time: 381 ms
CPU times: user 9.7 ms, sys: 0 ns, total: 9.7 ms
Wall time: 9.57 ms
size: 1.692e+06 😻 👍 🏿 < title > Document Title</title > :( < html><h2>2nd levelheading</h2></html > [email protected] [email protected] [email protected] ca n't bc$$ ef$4 5 66 _CUR_ wo nt seven eihtg _CUR_ nine _CUR_ _CUR_ zer$ 😻 👍 🏿 < title > Document Title</title > :( < html><h2>2nd leve

24. NLP text preprocessing: Removing e-mail address (spacy)

%time tokens = [token for token in long_s_doc if not token.like_email]
print('size: {:g} {}'.format(len(tokens),tokens[:int(text_l/3)]))

output =>

CPU times: user 52.7 ms, sys: 3.09 ms, total: 55.8 ms
Wall time: 54.8 ms
size: 99999

About 0.06 second for 1 million characters.

25. NLP text preprocessing: Remove whitespace and punctuation (spaCy)

%time tokens = [token.text for token in long_s_doc if (token.pos_ not in ['SPACE','PUNCT'])]
%time text = ' '.join(tokens)
print('size: {:g} {}'.format(len(text),text[:text_l]))

26. NLP text preprocessing: Removing stop-words

NLP models (ex: logistic regression and transformers) and NLP tasks (Sentiment Analysis) continue to be added. Some benefit from stopword removal, and some will not. [2]

Note: We now only use different deep learning language models (transformers) and do not remove stopwords.

%time tokens = [token.text for token in long_s_doc if token.is_stop == False]
%time long_s = ' '.join(tokens)
print('size: {:g} {}'.format(len(long_s),long_s[:text_l]))

27. NLP text pre-processing: Lemmatization

Lemmatization looks beyond word reduction and considers a language’s full vocabulary to apply a morphological analysis to words.

Lemmatization looks at the surrounding text to determine a given word’s part of speech. It does not categorize phrases.

%time tokens = [token.lemma_ for token in long_s_doc]
%time long_s = ' '.join(tokens)
print('size: {:g} {}'.format(len(long_s),long_s[:text_l]))

output =>

CPU times: user 366 ms, sys: 13.9 ms, total: 380 ms
Wall time: 381 ms
CPU times: user 9.7 ms, sys: 0 ns, total: 9.7 ms
Wall time: 9.57 ms
size: 1.692e+06 😻 👍 🏿 < title > Document Title</title > :( < html><h2>2nd levelheading</h2></html > [email protected] [email protected] [email protected] ca n't bc$$ ef$4 5 66 _CUR_ wo nt seven eihtg _CUR_ nine _CUR_ _CUR_ zer$ 😻 👍 🏿 < title > Document Title</title > :( < html><h2>2nd leve

Note: Spacy does not have stemming. You can add if it is you want. Stemming does not work as well as Lemmazatation because Stemming does not consider context [2] (Why some researcher considers spacy “opinionated”).

Note: If you do not know what is Stemming, you can still be on the Survivor show. (my opinion)

Conclusion

Whatever the NLP task, you need to clean (pre-process) the data (text) into a corpus (document or set of documents) before it is input into any NLP model.

I adopt a text pre-processing framework that has three major categories of NLP text pre-processing:

  1. Noise Removal
  • Transform Unicode characters into text characters.
  • convert a document image into segmented image parts and text snippets [10];
  • extract data from a database and transform into words;
  • remove markup and metadata in HTML, XML, JSON, .md, etc.;
  • remove extra whitespaces;
  • remove emoji or convert emoji into phases;
  • Remove or convert currency symbol, URLs, email addresses, phone numbers, hashtags, other identifying tokens;
  • The correct mis-spelling of words (tokens) [7];
  • Remove remaining unwanted punctuation;

2. Tokenization

  • They are splitting strings of text into smaller pieces, or “tokens.” Paragraphs segment into sentences, and sentences tokenize into words.

3. Normalization

  • Change all characters to lower case;
  • Remove English stop words, or whatever language the text is in;
  • Perform Lemmatization or Stemming.

Note: The tasks listed in Noise Removal and Normalization can move back and forth. The categorical assignment is for explanatory convenience.

Note: We do not remove stop-words anymore. We found that our current NLP models have higher F1 scores when we leave in stop-words.

Note: Stop-word removal is expensive computationally. We found the best way to achieve faster stop-word removal was not to do it.

Note: We saw no significant change in Deep Learning NLP models’ speed with or without stop-word removal.

Note: The Noise Removal and Normalization lists are not exhaustive. These are some of the tasks I have encountered.

Note: The latest NLP Deep Learning models are more accurate than older models. However, Deep Learning models can be impractically slow to train and are still too slow for prediction. We show in a follow-on article how we speed-up such models for production.

Note: Stemming algorithms drop off the end of the beginning of the word, a list of common prefixes and suffixes to create a base root word.

Note: Lemmatization uses linguistic knowledge bases to get the correct roots of words. Lemmatization performs morphological analysis of each word, which requires the overhead of creating a linguistic knowledge base for each language.

Note: Stemming is faster than lemmatization.

Note: Intuitively and in practice, lemmatization yields better results than stemming in an NLP Deep Learning model. Stemming generally reduces precision accuracy and increases recall accuracy because it injects semi-random noise when wrong.

Read more in How and Why to Implement Stemming and Lemmatization from NLTK.

Text preprocessing Action benchmarks

Our unique implementations, spaCy, and textacy are our current choice for short text preprocessing production fast to use. If you don’t mind the big gap in performance, I would recommend using it for production purposes, over NLTK’s implementation of Stanford’s NER.

In the next blogs, We see how performance changes using multi-processing, multithreading, Nvidia GPUs, and pySpark. Also, I will write about how and why our implementations, such as EMOJI_TO_PHRASEand EMOJI_TO_SENTIMENT_VALUE and or how to add emoji, emoticon, or any Unicode symbol.

References

[1] How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read.

[2] Industrial-Strength Natural Language Processing;Turbo-charge your spaCy NLP pipeline.

[3] NLTK 3.5 Documentation.

[4] Textacy: Text (Pre)-processing.

[5] Hugging Face.

[6] Language Models are Few-Shot Learners.

[7] re — Regular expression operations.

[8] Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm.

[9] How I Built Emojitracker.

[10] Classifying e-commerce products based on images and text.

[11] DART: Open-Domain Structured Data Record to Text Generation.

[12] Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT.

[13] fast.ai .

[14] 1000x faster Spelling Correction.

This article was originally published on Medium and re-published to TOPBOTS with permission from the author. Read more technical guides by Bruce Cottman, Ph.D. on Medium.

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Microsoft BOT Framework: Building Blocks

I wrote an article last week introducing the ‘’Microsoft BOT Framework”. The highlight of the article was to educate the readers on how to…

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Photo by Tincho Franco on Unsplash

I wrote an article some weeks ago introducing the ‘’Microsoft BOT Framework”. The highlight of the article was to educate the readers on how to develop a basic chatbot. Although my workmates acknowledged the efforts but were interested in knowing more. In this article, I am going to dig in a little deeper with the various concepts involved with the Microsoft BOT framework.

I would be touching on the below-mentioned concepts in this article.

  • Channel
  • State
  • Prompt
  • Dialog
  • Waterfall
  • Connector
  • Activity
  • Turn

Channel

Channel is an application that is being used to interact with the BOT. Some of the current integrations are available with Teams, Slack, Workplace, Skype, Facebook, Telegram, Line, Webchat, etc.

Some channels are also available as an adapter. Check here for more details.

State

State in the context of the ChatBots means persisting metadata of the conversation between the BOT and the user at a certain moment. State management makes the conversation more meaningful (i.e the responses could be saved to be accessed at a later point of time.

Prompt

During a conversation between the user and the BOT, a prompt is an event when BOT asks a user any question. This question could be in the form of text, button, dropdown, etc.

Dialog

Dialogs allow forming flow in the conversation. A Dialog comprises of 2 steps.

  1. A prompt from the BOT requesting for info
  2. User Response to the BOT

If the user response is valid, BOT will send a new prompt for further information, else it will re-send the same prompt.

1. 8 Proven Ways to Use Chatbots for Marketing (with Real Examples)

2. How to Use Texthero to Prepare a Text-based Dataset for Your NLP Project

3. 5 Top Tips For Human-Centred Chatbot Design

4. Chatbot Conference Online

Waterfall

The waterfall is formed with a combination of Dialogs. It’s a sequence of dialogs which determines the complete flow of the conversation.

Let’s look at all of these concepts in a diagrammatic representation.

Connector

REST API used by BOT to communicate across multiple channels. The API allows the exchange of messages between BOT and the user on a specific channel.

Activity

As the name suggests, an activity is any communication between the user and the BOT. The connector API uses the activity object to send useful information back and forth. The most common activity type is the message. For a complete list of all Activity types, see here.

Turn

In any conversation between two parties, each party takes turns to respond to an activity (message). In the context of Microsoft BOT Framework, communication happens between user and BOT, hence a turn could be considered as the processing done by the BOT to respond to the user request.

Now that we have understood the basic concepts needed to build this sample, let’s have a look at our use case.

We would be building a ChatBot application which would enable users to Book a taxi. The conversational flow would be like:

Each box in the above diagram represents a Dialog.

Github: https://github.com/tarunbhatt9784/MFTSamples/tree/master/SuperTaxiBot

Step 1: Create a VS2017 project

I would set the name of the project as “SuperTaxiBot”.

Step 2: Install Nuget Package

Install Nuget Package Microsoft.Bot.Builder.Dialogs using VS2017.

Step 3: Create a DialogBot.cs

The class consists of bot logic which processes incoming activities from one or more channels and generates outgoing activities in response.

ActivityHandler defines various handlers for different types of activities. The activities used in this sample are:

  • OnTurnAsync: Handles any incoming activity.
  • OnMessageActivityAsync: Invoked when a message activity is received from the user. If overridden, this could potentially contain conversational logic. By default, this method does nothing.
  • OnMembersAddedAsync: Invoked when members other than this bot (like a user) are added to the conversation

Source: https://chatbotslife.com/microsoft-bot-framework-building-blocks-377be3d55dab?source=rss—-a49517e4c30b—4

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