Semantic Video Tool (SVT), project assignment presentation for the course on Information Retrieval and Text Mining of the Master course in Artificial Intelligence o Maastricht University. The assignment consists in the realisation of webbased application entitled Semantic Video Tool (SVT for short). Given the transcripts of more than 1300 videos web-crawled from TEDTalk, SVT is able to analyse the content of the videos dynamically through a number of natural language processing and text-mining operations. For each video it extracts keywords and it generates automatic summaries and valuable insights; it also works as a search and a recommendation engine. For more information please visit http://www.dimstudio.org/semantic-video-tool/

1. Semantic Video Tool
IRTM - Assignment Presentation
28th May 2015 Daniele Di Mitri

2. SVT in a nutshell
• video directory
• RESTful web application
• NLP semantic video analyzer
• video search engine
• analytics
Using the
video
transcripts!

3. Corpus
• 1391 video transcripts from TEDTalks
• from the 6 top categories
– technology, business, design, entertainement, science, global issues
• + related metainfo
– no. comments, no. views, category, length, etc.
Why TEDtalks?
• several documents
•  equal length
• very good English

5. NLP operations
• Common NLP operations (NLTK)
– Tokenization (punctuation)
– POS Tagging
– Stemming
– Chunking
– Frequent monogram, bigram, trigrams
• Automatic summaries (in 2 sentences)
• TF-IDF based search (powered by scikit-learn)
• Popular video classification
• Anaphora resolution

6. TF-IDF based
reccomending system
TED
SVT

7. Why is Monica Lewinsky popular?
comments views
datediff(now,dateup)
Log
Popularity Rate =
• Idea: mark as «popular» docs with rate>15 and «unpopular» the rest
• HOW: pipeline SVM & TF-IDF to classify (work in progress)

8. Anaphora handling
• RegExp (b(?![a-zA-Z]{2}s)w+|')+

9. Demo!