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Adding value to NLP: a little semantics goes a long way.
Published on Jul 19, 201957 Views
Natural language processing technology is now ubiquitous, even if there are still many challenges to be faced in its development. From sentiment analysis to machine translation to chatbots; from medic
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Chapter list
Adding value to NLP: a little semantics goes a long way 00:00
15 years ago in NLP, we were00:13
NLP at ESWS 200401:34
Mapping semantic relations02:17
Merging descriptions of plants in Multiflora02:49
Semantic annotation 15 years ago at Ontotext03:23
And at the OU03:41
Many of the underlying technologies are still relevant03:52
How to impress a “freedom of the media” researcher06:11
Feral databases06:49
That’s great, we have lots of NLP tools to do that!07:43
Woe betide anyone who opens the sacred spreadsheet!08:04
Harry Potter and the Spreadsheet of Doom08:38
Once the latest in calendar technology09:13
Case studies: how can NLP and semantics help?09:47
Global monitoring of violations against journalists09:56
Events are complex12:14
But we know how to understand complex events13:08
Information categories in current monitoring efforts13:53
ICCS Crime Classification Scheme14:56
HURIDOCS classification scheme15:12
Putting it all together15:43
Reconciling information from different sources17:04
Reconciling our information18:24
Automatic categorization of free text 18:44
Social Media and Disaster Relief19:00
Aid workers in Nepal discussing strategy 19:57
Tools to help disaster victims get aid quickly: pinpointing geographic locations20:44
CREES Google Sheets Add-on22:13
Semantic Technologies in Scientometrics23:04
What kind of questions do we want to answer?24:59
Which countries published most about waste management and recycling in 2014?25:27
All publications25:53
How is European knowledge distributed across regions?26:52
Technological vs scientific knowledge production in genomics27:40
Specialisation Indexes in Biotechnology around Europe28:25
The Semantic Approach28:50
Ontologies connect information29:10
From ontology to data29:39
Creating and populating the ontology30:28
SGC Topics and SubTopics30:44
Linking information from external sources31:01
Linking information from external sources - 231:08
Ontology population31:34
Annotating Data with Ontologies32:05
Annotating Data with Ontologies - 232:49
Example of patent annotation33:13
Ongoing Challenges33:38
GATE MIMIR and Prospector34:35
GATE MIMIR and Prospector - 235:19
Prospecting Biomedical Literature36:37
Choosing A Specific Instance37:04
What diseases are in these documents?37:11
What pathogens?37:29
Disease vs Disease Co-ocurrences37:34
Diseases vs Pathogens38:06
Social media and politics38:27
Hate speech against politicians Jan-Feb 201938:58
Who gets the abuse?39:47
Top abusive words41:07
Words vs people41:40
Topics of abuse per party42:17
Local and national news coverage of Brexit43:24
So where are we at?46:06
Acknowledgements48:10