Hi Lars, Thank you for a great introductory video. I liked your teaching style. Rather than teaching individual components step by step, you actually gave an end-to-end introduction to all individual components and how they work together. I would say, your approach made the video very interesting. I look forward to your next videos. It would be great if you could show a practical example of the same project you described.
Hi, thanks for your positive input! What you describe was indeed precisely the idea of the video: give an end-end-to end introduction to all the different parts of text mining. Later lectures can then go more in depth with one specific topic each (e.g. dictionary-based named entity recognition). Aside from working well from a teaching perspective (give the big picture first), I think it also works better in a UA-cam setting, where long lectures are really not a great format. Regarding use cases, I have a hands-on tutorial available online already. I had just forgotten to link to it in the video description - I have fixed that now. But perhaps some video with a use case could be good to have too, I'll think about that.
@@larsjuhljensen Yes, In the long run, it would be interesting for a wider audience including me. For example, I saw AstraZeneca is using Neo4j graph database to integrate text, and molecular data to generate new insights. Although I am a novice in this field, I am curious to learn how we can understand cancer resistance by making use of literature and network data to pave way for combination therapy. But, of course, as I said before, it was just an idea for your future video :). I will first wait for a detailed walk-through of the project you already described in this video. I would also be happy if I could do some projects in the future with you after learning all the tools. Thank you :) You might find these interesting: 1) www.nature.com/articles/srep17417 2) pubmed.ncbi.nlm.nih.gov/27879272/
Fantastic videos so far Lars, you have done the world a service.
Thanks for the kind words!
Just discovered this channel with this video. Great thanks! Suscribed!
Thank you!
Great ! You are very active scientist.
Hi Lars, Thank you for a great introductory video. I liked your teaching style. Rather than teaching individual components step by step, you actually gave an end-to-end introduction to all individual components and how they work together. I would say, your approach made the video very interesting. I look forward to your next videos. It would be great if you could show a practical example of the same project you described.
Hi, thanks for your positive input! What you describe was indeed precisely the idea of the video: give an end-end-to end introduction to all the different parts of text mining. Later lectures can then go more in depth with one specific topic each (e.g. dictionary-based named entity recognition). Aside from working well from a teaching perspective (give the big picture first), I think it also works better in a UA-cam setting, where long lectures are really not a great format. Regarding use cases, I have a hands-on tutorial available online already. I had just forgotten to link to it in the video description - I have fixed that now. But perhaps some video with a use case could be good to have too, I'll think about that.
@@larsjuhljensen Thank you. I will have a look. Please do consider knowledge graphs in the long run.
@@pregograzie1672 As a video topic, you mean?
@@larsjuhljensen Yes, In the long run, it would be interesting for a wider audience including me. For example, I saw AstraZeneca is using Neo4j graph database to integrate text, and molecular data to generate new insights. Although I am a novice in this field, I am curious to learn how we can understand cancer resistance by making use of literature and network data to pave way for combination therapy. But, of course, as I said before, it was just an idea for your future video :). I will first wait for a detailed walk-through of the project you already described in this video. I would also be happy if I could do some projects in the future with you after learning all the tools. Thank you :)
You might find these interesting:
1) www.nature.com/articles/srep17417
2) pubmed.ncbi.nlm.nih.gov/27879272/