AI in Retail and E-Commerce: 30 Examples to Know
Management AI: Natural Language Processing (NLP) And Natural Language ...
One of my biggest complaints about terminology in the industry is the claim that data from conversations is "unstructured data". That is nonsense. After all, how do people communicate, either in voice or in a written language, if there was no structure that aids meaning? Syntax is the structure of language, and it clearly aids in defining semantics, or the meaning of the communications. To understand how computers are rapidly improving, it's important to look at how natural language is different from what computers have historically processed.
From flat file sequential data storage models to relational databases (RDBMS), there is a decade's long history of rigidly structured data. To people used to such formats, language seems to be highly unstructured, which led to the use of the wrong term. The rapid growth of cloud-based, text and voice, conversations confused many in the traditional database world. Still, it is past time to stop referring to unstructured data. Another, more accurate phrase, is loosely structured information (or data, if people wish to be less accurate but more comfortable).
In comparison, artificial intelligence (AI) has been focused on imitating human thought, communications, and actions. Almost from the beginning of the discipline of AI, researchers have been interested in how humans communicate. That has led to the two overlapping disciplines of natural language processing (NLP) and natural language generation (NLG).
Syntax and SemanticsThe limitations of earlier computing technology meant that much of the original work in AI on language was done via expert systems. The task was to understand language by defining rules that were executed by the system. The problem was that those rules were focused almost solely on syntax. While that could solve a lot of the problem, languages are fluid and semantic meaning isn't always found in syntax, and sometimes the syntax was harder to detect than expected. One of the funniest yet relevant examples of the blend between syntax and semantics is in helping computers understand the different meanings of the following two sentences:
The expanding number of rules slowed systems and didn't get to the high level of accuracy required in conversation.
The technology at the time also meant that the focus of language was on written language. In addition, it was easier to create syntactically correct output than to read the way we write, so the focus was on the complexity of NLP while NLG was often kept very simple. That was often why it was easy to get expert systems to fail the Turing Test, as the way people could twist language to confuse the systems and stilted, basic machine responses meant it was easy to tell that the conversation was with an expert system and not a human.
The Artificial Neural Network Advances Natural LanguageThe advances that brought cloud computing also helped natural language work. The processing power of clusters of computer and processors meant that more complex analysis could be done much faster. That brought artificial neural networks (ANN) to the front of the machine learning world. The ANN didn't have to explicitly define all syntactic rules and link them to semantics. By creating different network layers to analyze more basic components of language, the programmers could then let systems learn by example, using large volumes of text and speech. That led to both faster and more accurate NLP and NLG.
On the natural language processing side, that has allowed systems to far more rapidly analyze large amounts of text data. That has led to advances in internet search capacity, customer service sentiment analysis, and in multiple other areas. One example is in merchandising. There's a large volume of information in any major retailer's technology infrastructure. Expecting to get hundreds of merchandising agents to learn how to use a complex business intelligence (BI) interface or all become experts at pivot tables is a non-starter. The software becomes, as referred to in the on-premises days, as shelf-ware – software paid for but not used.
"Merchandisers being able to type ad-hoc queries as they think of the questions is critical to their business success," says Doug Bordonaro, Chief Data Evangelist for ThoughtSpot. "Natural language processing is critical to helping non-technical merchandisers be merchandisers rather than programmers. One of our Fortune 100 clients is now running over 10,000 searches each week and getting daily insight into sales trends, market basket assortment, and product profitability."
The advances have also helped in audio, in voice recognition and response. Speech is more informal than writing. In addition, changes in volume and accent can have a strong effect on understanding. While this decade has seen large advantages in understanding voices Silicon Valley knows (mainly, American male), databases are only now being expanded to understand a wider variety of spoken language.
ANNs are also helping on the language generation front. While it's primarily the performance of the systems that is allowing more rapid response to spoken language, NLG is also being helped by the more flexible neural network structure. Generated speaking styles not limited to rigid, syntax-driven, rules provide a more natural customer experience.
Another key area of NLG is in voice and text that can help explain other types of output, such as visualizations seen in many BI dashboards. "Visualizations are very powerful, but they're not sufficient," says David Judge, VP Leonardo and Analytics at SAP . "Images and text work together to communicate to a manager. Having an NLG solution that dynamically builds text to assist understanding is a powerful advantage. They also open up accessibility to many other channels where visuals are not possible or for people who can't use visual interfaces."
It Is Not One Or The OtherThe key to understanding NLP and NLG is that they are a pair. Systems that can understand and communicate in more natural language can speed the process of analysis and decision making. People learn in multiple ways. Words and images both have a place in the business analytics environment, so expect to see natural language tools penetrate much further into the market in the next two years.
A Beginner's Guide To Natural Language Processing And Generation - TNW
This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI.
20 years ago, if you had a database table containing your sales information and you wanted to pull up the ten most sold items in the past year, you would have to run a command that looked like this:
SELECT TOP 10 SUM(sale_total) AS total_sales FROM sales WHERE sale_date > DATEADD(day, -365, GETDATE()) GROUP BY item_idToday, performing the same task can be as easy as writing the following query in a platform such as IBM Watson:
Which 10 items sold the most in the past year?
From punch cards to keyboards, mice, and touch screens, human-computer interfacing technologies have undergone major changes, and each change has made it easier to make use of computing resources and power.
But never have those changes been more dramatic than in the past decade, the period in which artificial intelligence turned from a sci-fi myth to everyday reality. Thanks to the advent of machine learning, AI algorithms that learn by examples, we can talk to Alexa, Siri, Cortana, and Google Assistant, and they can talk back to us.
Behind the revolution in digital assistants and other conversational interfaces are natural language processing and generation (NLP/NLG), two branches of machine learning that involve converting human language to computer commands and vice versa.
NLP and NLG have removed many of the barriers between humans and computers, not only enabling them to understand and interact with each other, but also creating new opportunities to augment human intelligence and accomplish tasks that were impossible before.
[Read: How quantum computers could make future humans immortal]
The challenges of parsing human language
For decades, scientists have tried to enable humans to interact with computers through natural language commands. One of the earliest examples was ELIZA, the first natural language processing application created by the MIT AI Lab in the 1960s. ELIZA emulated the behavior of a psychiatrist and dialogued with users, asking them about their feelings, and giving appropriate responses. ELIZA was followed by PARRY (1972) and Jabberwacky (1988).
Another example is Zork, an interactive adventure game developed in the 1970s, in which the player gave directives by typing sentences in a command line interface, such as "put the lamp and sword in the case."
The challenge of all early conversational interfaces was that the software powering them was rule-based, which means the programmers had to predict and include all the different forms that a command could be given to the application. The problem with this approach was that first, the code of the program became too convoluted, and second, developers still missed out plenty of the ways that the users might make a request.
As an example, you can ask the weather in countless ways, such as "how's the weather today?" or "will it rain in the afternoon?" or "will it be sunny next week?" or "will it be warmer tomorrow?" For a human, understanding and responding to all those different nuances is trivial. But a rule-based software needs explicit instructions for every possible variation, and it has to take into account typos, grammatical errors and more.
The sheer amount of time and energy required to accommodate for all those different scenarios is what previously prevented conversational applications from gaining traction. Over the years, we've become used to rigid graphical user interface elements such as command buttons and dropdown menus that prevent users from stepping out the boundaries of the application's predefined set of commands.
How machine learning and NLP solve the problem
NLP uses machine learning and deep learning algorithms to analyze human language in a smart way. Machine learning doesn't work with predefined rules. Instead, it learns by example. In the case of NLP, machine learning algorithms train on thousands and millions of text samples, word, sentences and paragraphs, which have been labeled by humans. By studying those examples, it gains a general understanding of the context of human language and uses that knowledge to parse future excerpts of text.
This model makes it possible for NLP software to understand the meaning of various nuances of human language without requiring to be explicitly told. With enough training, NLP algorithms can also understand the broader meaning of human-spoken or -written language.
For instance, based on the context of a conversation, NLP can determine if the word "cloud" is a reference to cloud computing or the mass of condensed water vapor floating in the sky. It might also be able to understand intent and emotion, such as whether you're asking a question out of frustration, confusion or irritation.
What are the uses of NLP?Digital assistants are just one of the many use cases of NLP. Another is the database querying example that we saw at the beginning of the article. But there are many other places where NLP is helping augment human efforts.
An example is IBM Watson for Cybersecurity. Watson uses NLP to read thousands of cybersecurity articles, whitepapers, and studies every month, more than any human expert could possibly study. It uses the insights it gleans from the unstructured information to learn about new threats and protect its customers against them.
We also saw the power of NLP behind the sudden leap that Google's translation service took in 2016.
Some other use cases include summarizing blocks of text and automatically generating tags and related posts for articles. Some companies are using NLP-powered software to do sentiment-analysis of online content and social media posts to understand how people are reacting to their products and services.
Another domain where NLP is making inroads is chatbots, which are now accomplishing things that ELIZA wasn't able to do. We're seeing NLP-powered chatbots in fields such as healthcare, where they can question patients and run basic diagnoses like real doctors. In education, they're providing students with on-demand online tutors that can help them through an easy-to-use, conversational interface whenever they need them
In businesses, customer service chatbots use the technology to understand and respond to trivial customer queries and leave human employees to focus their attention on taking care of follow ups and more complicated problems.
Creating output that looks human-made with NLG
The flip side of the NLP coin is NLG. According to Gartner, "Whereas NLP is focused on deriving analytic insights from textual data, NLG is used to synthesize textual content by combining analytic output with contextualized narratives."
In other words, if NLP enables software to read human language and convert it to computer-understandable data, NLG enables it to convert computer-generated data into human-understandable text.
You can see NLG in power in a feature Gmail added a couple of years ago, which creates automatic answers for your letters using your own style. Another interesting use of NLG is creating reports from complex data. For instance, NLG algorithms can create narrative descriptions of company data and charts. This can be helpful data analysts that have to spend considerable time creating meaningful reports of all the data they analyze for executives.
The road aheadIn the beginning, there was a huge technical gap between humans and computers. That gap is fast closing, thanks in part to NLP and NLG and other AI-related technologies. We're becoming more and more used to talking to our computers as if they were a real assistant or a friend back from the dead.
What happens next? Maybe NLP and NLG will remain focused on fulfilling more and more utilitarian use cases. Or maybe they'll lead us toward real, Turing-complete machines that might deceive humans into loving them. Whatever the case, exciting times are ahead.
This article was originally published by Ben Dickson on TechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech and what we need to look out for. You can read the original article here.
Natural Language Processing: An Inside Look At How NLP Is Used In The ...
On Tuesday, December 20, 2016, Linde, a German supplier of industrial-gas products, announced that it was entering into a "merger of equals" with US competitor Praxair in a deal valued at $35 billion. The pairing had been talked about for months. The market reacted by dropping Linde's stock price from 162.15 to a low of 156.00, and Praxair's from 122.32 to 116.47.
But a subscriber of Bloomberg's Professional service was able to dig deeper. Using the terminal's "tren" function ─ short(er) for trend ─ users could get a series of different views of real-time trends on these two companies, specifically, relating to sentiment analysis. First, there's news sentiment ─ upwards of 1.2 million stories stream through the system each day, and the platform scores these stories based on positive or negative sentiment and the volume of stories relative to its average.
In terms of news volume, Linde was trending way up ─ over 100 percent greater than normal. While the market was down on the news ─ at this particular time, it was a little after 2 pm on the day of the announcement and its stock was down 4.18 percent ─ the news sentiment was slightly positive: +.09 rated on a scale from -1.00 to +1.00.
Next is the recently-added Twitter sentiment tab, which ingests a cultivated feed of about 500,000 Twitter accounts. The Twitterverse ─ including StockTwits ─ was neutral on the announcement ─ a nice, round 0.0.
Underpinning all of this information is a form of artificial intelligence (AI) known as natural language processing, or NLP.
"The automated intelligence is in what signals should be put out, because this puts out a lot of signals and sometimes you don't know what those signals mean because of the way that you've constructed your learning model and the way you're teaching your machine." Marc Alvarez, Mizuho Securities"What we found was that you couldn't apply the news sentiment algorithm to Twitter; they really are very distinct types of content that people use very different words to express themselves," says Adela Quinones, product manager of news and social media functionality at Bloomberg LP. "As a result, we chose to develop two different algorithms. For this, we use natural language processing and we also use support-vector machines to identify what the sentiment is. The news one, in particular, gets trained in over hundreds of thousands of story segments."
Historically, Bloomberg would index every piece of news using a rules-based system: which people were mentioned in the story, which companies, and what are the relevant topics? So, if a story flows through and Apple CEO Tim Cook is mentioned in it ─ or the iPad or iPhone ─ then "Apple" the company would automatically get tagged, as well. Depending on how many of these words were mentioned, the stronger the relevance it has to Apple.
"All of this has been done historically using rules. Now, increasingly what we're doing is using natural language processing to do this classification," Quinones says. "We started building the NLP-based system out about three years ago and are really starting to see the benefits in a big scale in the last year."
Ubiquitous, If Quiet
Natural language processing serves as a bridge between the raw, unstructured data and analytics platforms; basically, it's the melding of human and computer languages using AI and computation linguistics. As analytics platforms take on more importance, so too will natural language processing.
While it's not always so well known, NLP-underpinned technologies have seeped into many parts of everyday life. It's only logical, then, that it would creep into the capital markets: research, regulatory reporting, security, communication, portfolio management.
NLP ─ and its sibling, natural language generation (NLG) ─ is everywhere. But this decades-old AI is taking on greater importance because firms are now more easily able to suck in huge amounts of data, store it more cheaply, and distribute that information more easily. Additionally, cloud-based platforms and open-source projects have expanded NLP's scope of capabilities.
Marc Alvarez, chief data officer for Mizuho Securities, says that while there's already a whole universe of NLP technologies enveloping financial services as more data becomes available, more potential will arise. "We're getting our toes in," he says. "We use it to pick up sentiment from corporate press releases and earnings releases. That generates some interesting data."
Sentiment Analysis
Pluribus Labs was created two years ago and using AI and machine-learning techniques to extract signals from unstructured data. Everything that Pluribus delivers that's text-based is built using NLP tools.
The Berkeley, Calif.-based vendor uses AI to judge sentiment to predict outcomes and to analyze longer-form communications ─ i.E. SEC 10-K or 10-Q or Form 13F and earnings call transcripts.
"We use that sentiment ─ and aspects of the discussion that are not necessarily positive/negative sentiment, but more dispersion of opinions/volume of opinions ─ to predict outcomes of the market systematically," says Frank Freitas, founder and CEO of Pluribus. "We try to focus on taking the outcomes of that discussion to predict both volume and volatility in markets. What we want to capture is people saying things about either the security itself, executives that are associated with that entity, products associated with that entity, and then ─ based on capturing those right conversation points ─ arrive at a view of sentiment that's based on what real people are saying. That's where NLP comes in."
In the US, Pluribus runs sentiment analysis on social platforms like Twitter and StockTwits. In China, it curates Sina Weibo, a platform of 125 million users, many of whom are discussing market outcomes and individual securities. It is also working with a social media company based in Japan, and Australia will be next, says Freitas, who spent seven years as Instinet's COO. Pluribus' Deep Earnings platform offers a suite of metrics for every earnings call that happens for every company in the Russell 3000 Index.
To build out the platform, Pluribus had to create a proper finance dictionary.
"You need to be able to look at words in the context of parts of speech ─ where's the subject, where's the verb, where's the direct object, where's the indirect object?" he says. "Then you need, frankly, a dictionary that is machine-learned, or at least appropriate for the application that you're looking at. So for finance, we need to make sure that our dictionary loads on terms that have value for finance and that the terms themselves are scored right given their use in financial services."
Take, for example, the word "rip." Ninety-nine percent of the time it's used as a negative sentiment: You can rip your pants or rip a piece of paper. You can rip someone off. A rip-and-run is a crime. A fire can rip through a village. A bullet can rip a hole in your heart. I'm gonna rip your face off. But in the context of finance, rip is a positive outcome: The stock ripped up, meaning that it gained value rapidly.
"What we see is that by picking up the right words and the right valence of those words, we're accurately capturing the sentiment of the market," Freitas says.
Big for Small
Sitting just east of Lake Washington in the Pacific Northwest, Opal Advisors serves as a small fund managing just a few million dollars of assets. James Hua, Opal's founder and portfolio manager, left Freestone Capital Management ─ one of the largest registered investment advisors (RIAs) in the region ─ to try his hand at running his own shop. Knowing that he wouldn't be able to take advantage of all the tools he had at Freestone, he was referred by a colleague to AlphaSense.
AlphaSense offers a financial search engine that helps analysts and portfolio managers find info that's buried in dense text documents, "whether the information is in page 200 of an SEC filing footnote, or a broker research report, a piece of news or conference transcript," says Jack Kokko, CEO of the vendor.
Hua says that he uses the platform to generate investment ideas. "Usually, I have some companies that I'm interested in and then I'll do some searches around the company," he says. "If I read something, if I talk to another portfolio manager, if a company gets passed to me, the first thing I do is go to AlphaSense and read all the information they have: Qs, Ks, annual reports, presentation transcripts."
He says that the platform's NLP technology is very effective at highlighting and labeling relevant information.
"If I search for a certain word and they have a lot of synonyms attached to it, it gets underlined; there's a thesaurus database behind it," Hua says. "So, if I search 'revenue guidance,' guidance is underlined and it takes everything similar to guidance, such as 'given the range of' or 'projected.' I don't have to be super precise about the words that I'm searching for. ... Sometimes I might miss something. I might term it as 'risk,' but they term it as 'uncertainties' within the balance sheet. So they recommend terms that I otherwise might not have looked for."
Hua estimates that the platform cuts his search times down on an individual company from four or five hours to 30 or 45 minutes. Much like how Bloomberg has moved from a rules-based engine to an NLP-driven process, Kokko says this is evidence of a larger trend overall. This is also thanks to advancements in another form of AI, known as deep learning.
"When we started, we were still seeing a lot people applying rules-based systems. They were trying to cover human language with rules that would apply to every possible situation. We saw that running out of steam," Kokko says. "What's made this orders of magnitude more scalable is how deep learning has come in to give the machine the task of categorizing language and finding relationships nearly automatically. Deep learning takes in all that content and lets the algos learn and come up with patterns and rules that people wouldn't have thought of."
After Processing, Generating
The next evolution in this sector has seen the technology move beyond processing to generation, says Mizuho's Alvarez.
"More interesting for us is natural language generation. Where we're heading, we're trying to use our machine-learning capabilities to generate signals," he says. "The automated intelligence is in what signals should be put out, because this puts out a lot of signals and sometimes you don't know what those signals mean because of the way that you've constructed your learning model and the way you're teaching your machine. As we get more and more sophisticated and we put better input into the acceptance criteria into those signals, it's then about how do we deliver and express that for someone who needs to know about this."
As an example of how this is starting to look, every day Bloomberg produces a swath of short news stories that fall under the byline "Bloomberg Automation" ─ today, XYZ Company saw trading volumes slightly below average; in the past week the stock is up 5 percent; here's how it looks compared to other companies in the sector, etc.
While a behemoth with the scale and resources can invest in both NLP and NLG, most in the vendor space are picking sides. Narrative Science, for example, is firmly in the NLG market.
The Chicago-based company, through its Quill suite of solutions, focuses on three areas for content generation: institutional portfolio commentary, regulatory compliance and improving customer engagement.
Franklin Templeton Investments, for instance, is using Quill Portfolio Commentary to streamline the firm's fund commentary process. The platform allows Franklin Templeton's global marketing team to scale standard fund reporting coverage and frequency. This allows them to reallocate some of the group's resources to "focus on higher-value tasks, like producing white papers," says Kim Neuwirth, director of product management for Narrative Science.
Nick Beil, Narrative Science's COO, says that while the vendor is focused on NLG, that doesn't mean that partnerships with NLP companies are out of the question. After all, the future will see a blending of the techniques to make platforms more interactive and all-encompassing. Beil points to consumer products like Amazon Echo and Google Home ─ which use voice recognition to play music or answer questions ─ as a sign of what's to come.
"Look at the world of voice as a user interface," Beil says. "We're not 10 years away from someone saying, 'Amazon, how's my portfolio doing today?' Voice recognition and processing what is being asked, combined with Narrative Science service of language generation, drives conversation through that. Those technologies available through API services didn't exist five years ago the way they exist today."
Speaking the Lingo
In the late 1990s, while getting his PhD in computer science with a focus on computational neuroscience from Dublin City University, Tom Doris began working with natural-language interfaces, with a focus on generation. He has worked at Intel, Bear Stearns and Marshall Wace, and is currently the CEO of OTAS Technologies, which provides market analytics and trader support tools.
One of its products is Lingo, a natural-language reporting platform. Doris says the vendor really started to turn to natural language generation (NLG) once it realized it didn't have a good solution to alert users as to what happened overnight; they wanted a report or overview of the main metrics and highlights of the things most important to them in an easy-to-use format.
The key for OTAS wasn't just to convert the content to text; it was also about taking hundreds of individual indicators and boiling them down to the key indicators. He says the most challenging part of creating Lingo was the salience modeling, or finding metrics that are most interesting to humans, rather than oddities that aren't that odd. For example, how unusual is it for the options three-month implied volatility to be two times the industry average? The key is to use salience modeling to cut out most of the noise.
"You're basically creating narratives around conditions that have multiple factors feeding into them and it allows you to go that additional step to marry up with the hypotheses that the humans use to figure out when something is interesting or risky," Doris says.
Lingo gives traders an overview at the start of the day of the major things to watch for over the coming day or two, while providing something of a safety net so they are not blindsided by an event-something of a 50,000-foot view of the market.
On January 11, 2017, OTAS announced the launch of Intraday Lingo, which pairs automated natural language with microstructure charts to provide a textual report identifying moves and alerts, both in real time and historically. As the day unfolds, instead of just looking at charts for volume, liquidity and spreads, it provides a narrative that describes "blow-by-blow" what's happening in the market, Doris says.
He says that for NLG, it's important to not get too clever when developing reports. Trying to structure predisposed flair into the writ will only beget strain.
"We find that when text is machine generated, you don't want it to be completely dry, but you want it not to look like an attempt to mimic human text generation. When I use Excel to chart a price series, I expect the price series to look really good and generated by a computer and not like a drawing by a human being," he says. "A lot of people come into this thinking that they have to add in that arbitrary randomization to avoid appearing repetitive and therefore giving the game away; what we find is that users are very comfortable with that similar structure and begin to insist on it. They want the information to be where they expect it to be when they open that report."
Blending of Worlds
Like Bloomberg, Intel is one of those giants that walks both sides of the natural language street. Its Saffron Technology Group subsidiary offers a cognitive computing platform that "remembers" transitive relationships between entities over time, says Bruce Horn, CTO of Saffron Technology. "NLP allows us to take unstructured text and turn it into semantic entities that we can work on inside of Saffron," he says.
Among other things, Saffron's platform is used to report bugs and defects in software. If you have 1,000 developers trying to create and/or maintain a computer chip or piece of software ─ which involves debugging and putting those found defects into a "bug database" ─ it's more than likely that there will be overlap.
The developer writes what the problem is ─ such as the routines that were called, the version of the software, clock rates, etc. ─ and the Saffron system makes connections as to what that developer has written and compares that text to other requests in the database, looking for similarities. The system then alerts the developer that this bug has already been solved for and here's what was done, or it tells the developer that while this bug is not exactly recognized, it looks similar to a fix used by another group on another project.
Andrew Hickl, chief product officer for Saffron, says NLP helps to generalize, which is important because there are lots of different ways to correctly or incorrectly refer to different parts. It also helps to decode the complexity of human language, he says.
"Human language allows us shortcuts," Hickl says. "NLP and co-reference resolution and time normalization and geolocation normalization helps the system be more explicit and draw connections where they're supposed to be."
Finally, NLP allows users to more effectively look at relations between concepts and events. "To build these resources, NLP is the glue that allows these resources to provide value," he says.
The Test
Natural language processing dates back to the 1950s with Alan Turing ─ he of the famed Turing Test to see if a machine can exhibit intelligence and conversation that is indistinguishable from a human. Even though it's decades old, NLP ─ and, by extension, NLG ─ still has room to improve.
"NLP is still kind of a surface technology; there's not very much meaning storage or meaning understanding or memory process," adds Horn. "If I say, 'Michelle Obama's speech,' an NLP system will say, 'OK, I know Michelle Obama and I know what a speech is,' but does it know anything about the fact that Michelle gave an important speech two weeks ago? There's all this world knowledge that allows us in language to basically do a very low-bandwidth communication ─ Michelle Obama's speech ─ and have it activate a huge amount of context in your own mind."
But Horn also thinks that we're getting closer to a leap in evolution. "NLP is about to blast off with this new modeling of meaning, context and world knowledge that is starting to come together," he says.
Salient Points* While natural language processing has been around since the 1950s, the fact that there are more sources of unstructured text data, the ability to take in and store data more cheaply, and the growth of analytics platforms has seen more firms deploying NLP tools.
* These tools are used for everything from research to regulatory requirements to anti-money laundering and know-your-customer requirements.
*The next stage is natural language generation, where the platform takes in the information, processes the information, and then spits out reports and other documents to alert users to pertinent information.
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