Natural Language Processing to build machine chat with humans
#TechWatchbySeb - Weekly series of Tech sectors decrypted - Issue #24 - May 24th, 2021
Hello my friends 🖐,
and welcome back to the #TechWatchbySeb ☕️ - The weekly series of Tech sectors decrypted.
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Natural Language Processing to build machine chat with humans
For this week we will share insights on Natural Language Processing Technology while traveling in the UK 🇬🇧, Germany 🇩🇪, Portugal🇵🇹 and Spain 🇪🇸.
Recently I saw an incredible figure
266 billion
It’s the market size estimated by 2027 of the Artificial Intelligence market (from Fortune Business Insights, 2020). It is impressive but not that surprising as this technology is being implemented by more and more organisations in order to power customer engagement.
As you may know, there are a lot of sub-technology that composed Artificial Intelligence such as Machine Learning, Deep Learning, Neural network, or Natural Language Processing (NLP)…
As this market is incredible, I’ve decided to start a series of articles during which, I will review what is behind those sub-clusters or Artificial Intelligence.
This week we will have a deeper look at Natural Language Processing and what are the main Tech companies leveraging this technology in Europe.
Definition of Natural Language Processing and examples of Applications
Natural Language Processing is also defined by IBM as:
NLP combines computational linguistics — rule-based modeling of human language — with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment.
Part of the main tasks that can be delivered by advanced NLP:
Speech recognition, also called speech-to-text, is the task of reliably converting voice data into text data.
Part of speech tagging, also called grammatical tagging, is the process of determining the part of speech of a particular word or piece of text based on its use and context.
Word sense disambiguation is the selection of the meaning of a word with multiple meanings through a process of semantic analysis that determines the word that makes the most sense in the given context.
Named entity recognition, identifies words or phrases as useful entities. NEM identifies ‘Kentucky’ as a location or ‘Fred’ as a man’s name.
Co-reference resolution is the task of identifying if and when two words refer to the same entity.
Sentiment analysis attempts to extract subjective qualities — attitudes, emotions, sarcasm, confusion, suspicion — from text.
Natural language generation is the task of putting structured information into human language.
It is quite interesting to see that all those tasks can automate a lot of operations, today done by humans, but way faster and less expensive.
Now let’s have a look at 3 examples of applications of NLP:
Email filters: It started out with spam filters, uncovering certain words or phrases that signal a spam message.
Chatbot: Smart assistants recognize patterns in speech thanks to voice recognition and provide useful responses.
Language translation: Many languages don’t allow for straight translation and have different orders for sentence structure, which translation services used to overlook.
Tech ecosystem in Natural Language Processing
It is hard to find proper figures on NLP, but we can expect a growth correlated with all the AI companies. In a report from Cbinsight, we can see a strong evolution of investments in AI tech companies since 2014. As shown in the graph below it estimates a total of 2,235 deals in 2019 representing $26Bn of investments.
If we look more precisely at NLP companies, Crunchbase has referenced around 1,253 companies worldwide which have received around $6.4Bn of investments for the last 20 years. We can also read, in the graph below, that the most represented geography is by far the US. It is also interesting to see that in Europe the number of companies is quite important (350 in Europe compared to 550 in the US), but the investments are way more limited ($1Bn in Europe compared to $4Bn in the US).
What are the most funded European companies?
Unbabel 🇵🇹allows modern enterprises to understand and be understood by their customers in dozens of languages. The company has raised a total of $91M.
CloudFactory 🇬🇧 is a distributed workforce company for automating business processes. The company has secured $72M of fundraising.
Streetbees 🇬🇧 is a human intelligence platform that collects and analyzes offline consumer behavior. The company has secured a total of $63M.
What are the most active funds in NLP in Europe?
We can say that this market is still at an early stage, if we look either at the relatively small size of the most funded companies in Europe (as presented above), and also we can see in the graph below that this a strong portion of the deals were done to finance Seed and Early Stage companies.
However, we can see some active investors in Europe such as:
EASME 🇪🇺is the European Union executive agency for SMEs in charge of Enterprise Europe Network, COSME, and other programs. The fund has backed 11 NLP deals
Seedcamp 🇬🇧is a European seed fund that identifies and invests early in founders attacking global markets. They have backed 8 deals
Wayra 🇪🇸is a globally connected and technologically open innovation hub. They have supported 6 deals.
As a conclusion, we can easily see that the use of such technology will improve over time. The more mature NLP will be, the more adoptions we will see from customers. But this market is still quite early and strongly supported by public funds in Europe, thus it will be interesting to keep an eye on it.
So, that’s it for this week, wishing you a great week ahead🖐
Stay safe ❤️