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Question-Answering on News Video Archives: Techniques for Processing, Segmentation, and Correction

This talk discusses the challenges and approaches in question-answering on news video archives, including video processing, story segmentation, transcript correction, and personalized news retrieval. It explores techniques for accurate segmentation and correction of transcripts for effective question-answering. The talk also presents results and concludes with the future possibilities of natural human-oriented input/output in news video retrieval.

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Question-Answering on News Video Archives: Techniques for Processing, Segmentation, and Correction

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  1. Question-Answering ofLarge News Video Archives CHUA, Tat-Seng, Yang, Hui, Chaisorn, Lekha & Zhao, Yun-Long School of Computing National University of Singapore Email: chuats@comp.nus.edu.sg Web: http://www.comp.nus.edu.sg/~chuats

  2. Outline of Talk • Introduction and Motivation • News Video Processing & Story Segmentation • Video Transcript Correction • Question-answering on News Video • Results • Conclusion

  3. PersonalizedNews Video Retrieval • Infotainment, including news video, is one of the major applications of MM Technology • In a personalized news video scenario, users “interact” with the system to enquire info such as: • show me latest news video on Iraq  “Iraq” • highlight of last nights European football  “European football” • Results are time-specific • Users increasingly want to see video news, supplemented with audio and text • and summarized to as much detail as is necessary • In a more futuristic setup, these will be accomplished through “natural” human-oriented I/O

  4. Issues to Resolve • Imprecision of users queries • “highlight of football match last night?” • Extraction of semantic contents of video: • Multi-modality • Multi-sources • Segmentation of news video into story units with genre classifications • Summarization of info for viewing at different level of details

  5. What Kinds of Data Do we Have? • Most research in the past has looked into only one source • Example, video and its accompanying audio track, + ASR • In most real-life applications, information is readily available in multiple sources: • Broadcast news -- video and audio • Web-based news articles (by news stations) • On-line wired news (by news agencies) • Other general resources: ontologies, dictionary etc… • Other types of info increasingly used in IR community: • User models: query logs, user profiles etc. • A challenge in developing usable systems .. How to use these available data effectively In co-training/ testing type framework?? • Ignoring these obvious data resources will result in unsatisfactory solutions.

  6. Outline of Our Approach • In this talk, I will describe our approach in developing systems to handle large scale video corpuses – TREC video • Sources of data used: • News video itself: visual, audio features, ASR • External sources: on-line news articles of the same period • General resources – ontology of countries, dictionary - WORDNET • Approach (see architecture):

  7. Stage 1: Stage 2: Stage 3: Stage 4: Stage 5: Stage 6 System Architecture of VideoQA Overview of QA on News Video

  8. Outline of Talk • Introduction and Motivation • News Video Processing & Story Segmentation • Video Transcript Correction • Question-answering on News Video • Results • Conclusion

  9. Video Story Segmentationfor News Video • First basic problem: break the news video into meaningful units based on stories. Issues: • How to classify shots into the correct class/category? • How to detect story boundaries? • Most news adopt the structure similar to CNN’s (?)   

  10. Video Story Segmentationfor News Video -2 • To help alleviate the estimation problem in statistical learning, we adopt a two stage process: • Stage 1: Shot classification • Stage 2: Scene segmentation & classification • The set of features considered • Visual (color histogram, b/g change) • Temporal [Motion activity, Audio type, Shot duration, speaker change] • Mid-Level [# of Faces, Shot type, # of Text Lines, and text-position, cue phrases]

  11. Stage 1: Shot Classification • Divide video sequence into shots • Consider 13 categories of shots • Intro/Highlight • Anchor; 2-Anchor; Meeting; Speech • Still image shot; Text Scene • Sports; Live reporting • Finance; Weather; Commercial; Special • Perform classification using Decision Tree (SEE 6.0)

  12. Stage 2: Scene Detection • Employ Hidden Markov Model (HMM) to detect story boundaries • Features (sequence level features) used at this stage: • Shot classes – shot tags • Scene change [c/u] • Speaker change [c/u] • Cue phrases at the beginning of new stories • Input to HMM: [1cc 1uu 1cu ..2cc 4c 4uu 6uu 6uu …. 2cc …. ] • Tested on 120 hours of TREC video and achieve around 76% in F1 accuracy in story segmentation • TREC data may be down-loaded from TREC web sites later (?) (Chaisorn & Chua et al, ICME’02, WWW Journal’02, TREC’03)

  13. Outline of Talk • Introduction and motivation • News Video Processing & Story Segmentation • Video Transcript Correction • Question-answering on News Video • Results • Conclusion

  14. Text Transcript: from Speech to Text • Need accurate transcript for QA • not a problem for document or story retrieval • Performance of speech recognition system • Accuracy about 80% for news • Most errors are named entities – likely answer targets (ATs) • Most such errors are type substitution  homonym problem • Examples: pneumonia  new area; Tony Blair  Teddy Bear • How to correct errors in ATs?  use phonetic sound matching to correct the errors • May use confusion matrix successfully used in spoken docm retrieval • Problem: low precision  match to many irrelevant phrases • One solution: limit scope of phonetic sound match • By utilizing on-line text news of same period (extract base noun phrases and named entities) – reasonable

  15. Use of External Resourceto correct Speech Errors • Extract all ATs from on-line news articles, Ai = (ai1,.. aiq) • Given video transcript Ti with a list of terms (ti1, .., tip) • The basic problem is then to select an aikAi to replace a sequence of terms sjTi that maximizes the probability: where sj contains one or more consecutive terms in Ti • Basic idea:use co-occurrence probabilities & phonetic matching to find most likely aikAi to replace sequence of terms sjTi,: a) Extract list of probable ATs using co-occurrence probabilities a) Matching at phonetic syllable level; b) Matching at confusion syllable string level (see Wang & Chua, ACL’03)

  16. Outline of Talk • Introduction and Motivation • News Video Processing & Story Segmentation • Video Transcript Correction • Question-answering on News Video • Results • Conclusion

  17. System Architecture of VideoQA Overview ofQA on News Video (Similar to our text-based QA work – Yang & Chua, SIGIR’03)

  18. Question Processing • Users typical issue short queries (several keywords): • “development in North Korea” • “match last night” • Query is ambiguous!! • Analyze the query • to extract: • Key terms in query • Likely answer target • NP & NE in query • Type of video genre • Temporal constraint • Duration constraint • Example: • “football match last night?” •  “football”, “match”  “football team” (ORG-NAME)  “football match”  SPORTS  LAST-NIGHT •  30 seconds (default)

  19. Query Reinforcement • The query, however, is ambiguous! • Use on-line news articles to provide the context (user independent) • Basic Idea: Given original query q(o): • Use web (or news sites) and dictionary – WordNet • Find terms (from web articles) co-occur frequently with q(o) • Extract semantically related terms from WordNet • Add high probability terms into q(0) to get q(1) • Expect q(1) to contain more context terms than q(0) • For the football example: we expect q(1) to also contain terms like: “arsenal”, “inter milan”, “soccer”, etc (the big match last night)

  20. Query ReinforcementAnother Example • q(0)=“What are the symptoms of atypical pneumonia?” • q(1)= “symptoms, pneumonia, virus, spread, fever, cough, breath, doctor”  Use q(1) to retrieve a list of news transcripts at story level

  21. Final score is: where αk=1 and wkj = {wnj, whj, wcj, wej, waj, wvj} • The top K sentences are selected as the candidate answer sentences based on Sij Candidate Sentence Extraction • For the retrieved transcript Ti, we select sentences Sentij that best match the user query as follows: • noun phrases, wnj • named entities, whj • original query words q(0), wcj • expanded query words q(1-0) = q(1) - q(0), wej • video genre, wvj

  22. Outline of Talk • Introduction • News Video Processing & Story Segmentation • Video Transcript Correction • Question-answering on News Video • Results • Conclusion

  23. Results • Use 7 days of CNN news video from 13-19 Mar 2003 • contained a total of 350 minutes of news video • retrieved about 600 news articles per day from the Alta Vista news web site during these 7 days • Designed 40 factoid questions • 28 general questions that are asked everyday • 12 questions are date-specific • Give a total of 208 questions • Results (To present in ACM Multimedia ’03)

  24. The video summary example (4 shots) is: Results -- Example • Query: “What are the symptoms of atypical pneumonia?”, • the 3-sentence window selected by the QA engine is • S1: He and his two companions are now in isolation and the one hundred and fifty five passengers on the flight were briefly quarantined. • S2: Symptoms include high fever, coughing, shortness of breath and difficulty breathing. • S3: But health officials say there's no reason to panic.

  25. Outline of Talk • Introduction • News Video Processing & Story Segmentation • Video Transcript Correction • Question-answering on News Video • Results • Conclusion

  26. Related Work • Research in correcting speech recognition errors • (ACL’03, EMNLP’02) • News story and dialogue segmentation (Columbia U) • (ICME’03, ACL’03) • Question-answering in text • (TREC’02, SIGIR’03) • Infomedia Project • Uses multi-modality features effectively, esp speech • Insufficient emphasis on external resources • Works on Video-TREC - Large scale testing • Collaboration with Ramesh jain (Georgia Tech) as part of Video Tagging Project • Employ TV-Anytime metadata for news (collaborate with ETRI Korea) • Automatic tagging of TV-Anytime metadata, and use it as basis for video QA

  27. Summary • Works are preliminary • Many processes needs to be automated • Participating in this year’s Video-TREC and test on large scale corpuses (120 hours of news video) • On both story segmentation and retrieval • Experience: • Story Segmentation: content features are important, text or ASR feature less important • Retrieval: Text or ASR is important; content features help in enhancing precision • Current Work: • Build appropriate meta model to encode domain knowledge • Use higher order statistics to analyze data • KEY MESSAGE– Must incorporate domain model and utilize multi-modality, multi-source information

  28. THANK YOU

  29. Question classification and possible video genres

  30. Question analysis

  31. List of Questions • Who is the British Prime Minister? • Who is elected to be China's President? • Who is the President of the United States? • What is the name of the former Premier of China? • What is the name of the new Premier of China? • Who will pay the heaviest tallies? • Who was arrested in Pakistan? • Which musician called off his US tour? • When will NASA resume shuttle flights? • When will Germany, France and Russia meet? • When is the funeral of DjinDjic? • Which are the three countries involved in the summit today? • Where was the summit held? • Which city is the capital of Central African Republic? • Which are the three major war opponent countries? • To whom US withdrew the aid offer? • Which country vowed to veto the resolution today? • Which country's compromise proposal was rejected by US? • Where is Kashmir Hotel? • Where did Iraq invite the chief weapons inspectors to?

  32. List of Questions – cont. 21. Which city has the largest anti war demonstration? 22. Where did a AL QUEDA suspect arrested? 23. How many people attended the rally in San Francisco? 24. What is the cost of war? 25. How many people were killed in a Kashmir Hotel? 26. How many people participated in the rally in Madrid? 27. How many people were killed by the new pneumonia? 28. What are the symptoms of the atypical pneumonia? 29. What sanction did President Bush lift? 30. What was the name of the space shuttle broken apart in February? 31. Which rally shows the support for President Bush? 32. What is the official name for the mysterious pneumonia? 33. Which company tests their new passenger profiling system? 34. Name one Jewish holiday. 35. What is British stance? 36. How did Serbs Prime Minister die? 37. How is the anti-war protest in Madrid? 38. How is tomorrow's weather? 39. What is the conflict between US and Turkey? 40. What does the WHO call the new pneumonia?

  33. Some Remarks onStory Segmentation Task • Our 2-stage approach helps alleviate the statistical estimation problem – requires less training data • Similar works done in Columbia U • Using maximum entropy method • For video segmentation (ICME’03) and dialogue segmentation (ACL’03) • Achieves similar performance • Our current work: • Integration of multiple machine learning methods: HMM, ME, heuristic rule methods, and co-training approach • Fusion of multiple modal features: visual/audio features, text (speech to text), meta-data + domain knowledge • Note: Use only text feature (ASR) performs badly

  34. Recall Precision • At each level, we compute: • LCS(qi,cj): gives longest common subsequence (LCS) match between aik and sj at phonetic syllable level in the order of their occurrence • Mk == I for Levels a and b match; and == coefficients of confusion matrix at Level c match Multi-tier mapping(Wang, Chua, ACL’03) • We perform matching at 2 levels to find the most likely aikAi to replace the sequence of terms sjTi,: a) Phonetic syllable level; b) confusion syllable string level

  35. Query Reinforcement • The query, however, is ambiguous! • Use on-line news articles to provide the context (user independent) • Basic Idea: Given original query q(o): • Go to web (or news sites) to retrieve top N documents • Extract terms with high co-location probabilities with q(o), Cq • Extract semantically related terms from WordNet, Gq & Sq • Extra terms to be added: Kq = Cq + (GqSq) • (q(1)= q(0)+{top m termsKq with weights>=σ} • Expect q(1) to contain more context terms than q(0) • For the football example: expect q(1) to also contain terms like: “real madrid”, “manchester united”, “soccer”

  36. Query ReinforcementAnother example • q(0)=“What are the symptoms of atypical pneumonia?” • q(1)= “symptoms, pneumonia, virus, spread, fever, cough, breath, doctor”  Use q(1) to retrieve a list of news transcripts at story level

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