Embed Size (px)
DESCRIPTION
Citation preview
Television in Words TIWO Round Table EPSRC GR/R67194/01
Softel18th September 2002
“TIWO News”
Visit from Prof. James Turner, School of Library and Information Science, University of Montreal
Contact from local company, Force10 - supplier of Low Vision Aids and Assistive hearing products ; and from Surrey Association for Visual Impairment
Andrew Vassiliou – PhD student starts October Mike Graham – MSc student, has started
“TIWO News”
Papers presented:– LREC 2002 Workshop on Temporal Information in
Natural Language– TKE 2002, Terminology and Knowledge
Engineering, ‘Words for Pictures: analysing a corpus of art texts’
– Banff New Media Institute, workshop on AI and New Media ‘Narrative in Multimedia Systems’
“TIWO News”
VACE – Video Analysis and Content Exploitation, Advanced Research and Development Activity (ARDA)
– automatic content detection and recognition technologies for two primary video data sources: video scenes of various indoor and outdoor activities involving people, meetings, and vehicles, and TV news broadcasts.
(1) indexing and retrieval for video data; (2) autonomous video understanding; (3) ancillary improvement for still image processing; (4) enabling technologies for video data mining, filtering and selection; and (5) a drastic reduction in volume for video storage and forwarding
mechanisms. http://www.ic-arda.org/InfoExploit/vace/
“TIWO News”
UniS GRID Project Proposal – Proposal for a GRID ‘Centre of Excellence’ at
Surrey: an infrastructure to support future projects– Focus on language based information and
knowledge access on the GRID– Future projects may include “TIWO 2” (alongside
projects in the areas of finance; criminal investigation; digital heritage; medical images, etc.)
– Currently inviting organisations to express support and register interest in future projects
Summary of Progress
Corpus Building and Analysis System Development “Narrative” – reading group over the summer
“Defining” Narrative
“a primary resource for structuring and comprehending experience”; “a discourse style and a cognitive style ”; “realised in combinations of media”
“a sequence of (causally) connected events, organised in space and time”
“usually the agents of cause and effect are characters”“audience creates a richly represented fictional world”“viewer recalls information, anticipates what will follow, infers events
not explicitly mentioned / depicted”“narrative comprehension involves mental stores and inferences in
relation to: text-specific knowledge, world knowledge and knowledge of genre”
“Computing” Narrative
Video data models tend to comprise entities, events, actions and spatio-temporal relations; may with to add AI to deal with further aspects of narrative…
Knowledge-bases for text-specific and world knowledge, including stereotypical situations
Representing characters “psychological drives” Representing and reasoning about intentions /
emotions Maintaining belief models and perspectives;
viewer, machine and characters
Cautionary Note
“More than reconstructed timelines and inventories of existents, storyworlds are mentally and emotionally projected environments in which interpreters are called upon to live out complex blends of cognitive and imaginative response, encompassing sympathy, the drawing of causal inference, identification, evaluation, suspense, and so on”
David Herman, Story Logic (2002).
Analysis of “Narrative Features” in a Corpus of Audio Description Scripts
Focussed on emotive states by observing occurrences of words associated with emotional states in audio description scripts, e.g. JOY (happy, happily, pleasure, contentedly), DISTRESS (miserably, sadly), FEAR (anxiously, desperately), etc.
Resulting graphs characterise changing emotional states during a film…
Corpus Building and Analysis
Elia Tomadaki
Corpus linguistics and narrative
Any collection of more than one text can be called a corpus, the Latin equivalent for “body”. Thus, a corpus is any body of text. In the context of modern linguistics, it appears to have four basic characteristics: Sampling and representativeness, finite size, machine-readable form and a standard reference. Corpus linguistics deal with the study and use of language through corpora.
Linguists analyse corpora of narrative discourse and have observed features such as frequent reference to perfect aspect, third person reference etc. Therefore, this area of study is interesting for an AD corpus.
Corpus building
Type Num of scripts Num of words
Films 24 153,600
Series 8 17,600
Recipes 4 24,000
Children’s programmes
5 22,500
Documentaries 4 26,400
TOTAL 244,100
GATE system
English Patient AD: A comparison
Describer/s - Company Num of words
Louise Fryer and Michael Baker – ITFC
6,736
Di Langford - RNIB 7,436
Saul Zaentz - Saul Zaentz Company
31,560 (approx. 1,500 dialogue)
Most frequent wordsWord RNIB
frequencyITFC frequency
Saul Zaentz Company frequency
Hana 73 73 368
Almasy 81 74 340
Katharine 63 77 267
Patient 33 21 223
pilot 8 10 15
An example
Di Langford - RNIB
Louise Fryer and Michael Baker - ITFC
Saul Zaentz - Saul Zaentz Company
An explosion on the road ahead. The jeep has hit a mine (12 words)
the jeep explodes in a ball of flame. (8 words)
Suddenly an explosion shatters the calm as the jeep runs over a mine (13 words)
System Development
Yan Xu
Aims and objectives
Be able to browse, index video data based on inferences about the semantic content
Make the machine “understand” the story --narrative
Knowledge representation: build up general knowledge( CYC, Commonsense) and text-specific knowledge
Film
Film Editing
Scene
Title Sequence
End-credits
Shot
Text
Audio Description
Dialogue
Narrative
Time PropLocation Character Event State
Inferred Events
Explicitly Event
Non-diegetic Plot
Plot
Explicitly Event
Non-diegetic Plot
Text
Audio Description
Dialogue
Film
Film Editing
Scene
Title Sequence
End-credits
Shot
Narrative
Time PropLocation Character Event State
Inferred Events
Plot
Non-diegetic Plot
Explicitly Event
TextFilm
Narrative
Event
Inferred Events
Plot
Non-diegetic Plot
Explicitly Event
Feedback and any questions?
