MUMIS Multimedia Indexing and Searching Franciska de Jong & Thijs Westerveld University of Twente [email protected] OBJECTIVES • • • • • • • • Automatically indexing of video Data from different media sources (paper, radio, tv) Domain: soccer Digitise + ASR Extract significant events Merge annotations Store final annotations UI for searching FACTS SHEET Title: and Funding: MUMIS: Multimedia Indexing Searching Environment EU Language Engineering Sector of TAP Duration: 30 months July 2000 – January 2003 Volume: 2.4 M Euro, 385 Person months Languages:Dutch, English, German (Swedish) Consortium • • • • • • • University of Twente (NL) Sheffield University (UK) University of Nijmegen (NL) DFKI LT-Lab (DE) Max Planck Institute for Psycholinguistics (DE) Esteam (SE) VDA (NL) Offline Processing Information Extraction DE Formal Formal Formal Text Formal Text NL Formal Formal Text Text Formal Text Formal Text Formal Text EN Formal Formal Text Text Text Formal Text Text Merging Merged Annotated formal text Formal Formal Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Text Speech Text Signals ASR Formal Formal Formal Text Text Formal Text Formal Formal Text Text Formal Text Formal Formal Text Text Free Text IE Formal Text Formal Text Formal AnnoText tations Text Formal Formal Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Text Speech Text Merging Transcr Annotations Automatic Speech Recognition DOMAIN MODELLING DATA: text, video, audio ENTITY EVENT Location Time Person Score Object Date Player Defender Stopper Multilingual Lexicons Goal Player:… Artifact Cause:… Time:… ... Official RELATION Foul Player:… Consequence:… Time:… Annotations Multilingual IE Multilingual Search ... Location:... <?xml version=…> <mumis-ontology> <version>…</version> ... <class> <name>Defender</name> <documentation>a ’Defender’ is a …</documentation> <subclass-of>Player</subclass-of> </class> </mumis-ontology> SPEECH RECOGNITION • • • • Large-vocabulary Speaker independent Phoneme-based Hidden Markov models • acoustic model • language model • • • Emotionally coloured speech Domain language model Match specific vocabularies (player names) INFORMATION EXTRACTION • • • • • • multilingual formal descriptions closed captions tickers newspapers ASR output (radio/TV comment) IE DATA Ticker 24 Scholes beats Jens Jeremies wonderfully, dragging the ball around and past the Bayern Munich man. He then finds Michael Owen on the right wing, but Owen's cross is poor. TV report Newspaper Formal text Scholes Owen header pushed onto the post Deisler brought the German supporters to their feet with a buccaneering run down the right. Moments later Dietmar Hamann managed the first shot on target but it was straight at David Seaman. Mehmet Scholl should have done better after getting goalside of Phil Neville inside the area from Jens Jeremies’ astute pass but he scuffed his shot. Schoten op doel 4 4 Schoten naast doel 6 7 Overtredingen 23 15 Gele kaarten 1 1 Rode kaarten 0 1 Hoekschoppen 3 5 Buitenspel 4 1 Past Jeremies Owen IE Techniques & resources • • • • • • • 24 Scholes beats Jens Jeremies wonderfully, dragging the ball around and past the Bayern Munich man. He then finds Michael Owen on the right wing, but Owen's cross is poor. Tokenisation Lemmatisation POS + morphology Named Entities Shallow parsing Co-reference resolution Template filling He 24 24 NUM He 24 then finds Michael Scholes time Owen on then Scholes Scholes the right wing PROP then Scholes player finds beats beat beat VERB VP finds beat PASS Michael Jens sing Michael Jens Jeremies 3p player player1 Owen Jeremies Jens = Scholes PROP Owen wonderfull player2 on wonderfully wonderfull Jeremies = Owen.PROP on , ,the wonderfull the … right right wing wing ADV NP but dragging drag , PUNCT … Owen's ... ... cross NP MERGING • • • • Fuse annotations and recover from errors and differences: Multiple annotations of the same event (possibly with different attributes, e.g. time). Wrong event descriptions because of information extraction errors. Merging multiple partial annotations, e.g. by solving unsolved references like “star player”. ON-LINE TASKS Multilingual Search and Display •Search for interesting events with formal questions (user interface in many languages) •Indicate hits by thumbnails & let user select scene •Play scene via the Internet & allow scrolling Give me all goals from Overmars shot with his head in 1. Half. Event=Goal; Scorer=Overmars; Cause=Head; Time<=45 PSV - Ajax 1995 Ned - Eng 1998 Ned - Ger 1998 SUMMARY • Multimedia and multilingual • ASR on emotionally coloured speech • IE on ASR output • Merging different annotations • Search archives and play video online http://parlevink.cs.utwente.nl/projects/mumis.ht
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