This is the course page for Topics in Artificial Intelligence / Semantic Natural Language Processing, NoAI, and Big Knowledge (including dialog and AI) by Damir Cavar.
You might wonder, why does the title mention NoAI and Big Knowledge. The reason for NoAI is that the use of the term AI nowadays has drifted away from its core meaning, that we have to call our approach to artificial intelligence NoAI. The term Big Knowledge as opposed to Big Data should emphasize the fact that data as such is less relevant, at least for us here. What we are interested in is aggregation of information and the validation, transformation, conversion of information to knowledge. Big Data was a necessary step that involved a significant computational challenge that consequently leads us towards the real goal and challenge: Big Knowledge and all the related theoretical and computational challenges.
The goal of this seminar is to understand the limitations of NLP, Big Knowledge, NoAI (and the modern form of AI), to establish a research and education platform, and to design a research agenda for the next years.
This project is supported by Amazon AWS Educate with AWS Promotional Credits to the professor and various students.
We have set up an Amazon AWS EC2 server instance at the URL:
The server will provide more detailed information about the technologies and the current status of development. The general architecture involves:
In general, useful textbooks for beginners in speech and language processing, Computational Linguistics (CL), Natural Language Processing (NLP), or even AI (language related) are:
A good idea about very concrete topics in CL/NLP you can get from:
If you are interested in NLP, but you have little or no background in linguistics, a very nice and useful introduction is:
You might soon discover that linguistics is an absolutely interesting field and you might want to know more about your language, why you speak and how you do it, and so on. There is a large number of very good linguistic textbooks, courses, general learning material. One strategy is to consult your local linguist. Another is to check out linguistic programs, the courses covered, the course material etc. Here is a random selection of very good linguistic textbooks to start with:
There is also an interesting Wikibooks project as part of the Linguistics Collection, which might provide valuable information to start with: