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Multimedia Answer Generation for Community Question Answering

Community question answering (cQA) services have
gained popularity over the past years. It not only allows community
members to post and answer questions but also enables general
users to seek information froma comprehensive set of well-answered
questions. However, existing cQA forums usually provide
only textual answers, which are not informative enough for many
questions. In this paper, we propose a scheme that is able to enrich
textual answers in cQA with appropriate media data. Our
scheme consists of three components: answer medium selection,
query generation for multimedia search, and multimedia data selection
and presentation. This approach automatically determines
which type of media information should be added for a textual answer.
It then automatically collects data from the web to enrich the
answer. By processing a large set of QA pairs and adding them to
a pool, our approach can enable a novel multimedia question answering
(MMQA) approach as users can find multimedia answers
by matching their questions with those in the pool. Different from a
lot ofMMQAresearch efforts that attempt to directly answer questions
with image and video data, our approach is built based on
community-contributed textual answers and thus it is able to deal
with more complex questions.We have conducted extensive experiments
on a multi-source QA dataset. The results demonstrate the
effectiveness of our approach.

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Multimedia Answer Generation for Community Question Answering

  1. 1. Multimedia Answer Generation for Community Question Answering
  2. 2. Problem Statement • Textual Answers • Multimedia Answers
  3. 3. Literature Survey Sr. No Author Year Contribution 1. Trec: Text Retrieval Conference [http://trec.nist.gov ] 1990 Text based QA 2. S.A. Quarteroni, S. Manandhar 2008 Text based QA based on type of questions Open Domain QA 3. D. Molla & J.L Vicedo 2007 Restricted Domain QA 4. H. Cui, M.Y. Kan 2007 Definitional QA 5. R.C.Wang, W.W. Cohen, E. Nyberg 2008 List QA 6. H. Yang, T S chua, S. Wang 2003 Video QA 7. J.Cao, Y-C- Wu, Y-S Lee 2004- 2009 Video QA using OCR & ASR 8. Lot of authors 2003- 2013 Content Based Retrieval
  4. 4. System Decomposition • Answer medium selection, • Query generation • Multimedia data selection and presentation.
  5. 5. Pre-requisites • Datasets • Image & Video Mining API – Flickr, Picasaweb, Youtube, etc.
  6. 6. Process Flow Dataset collection Classification (Conversational & Informational) The answer medium selection and query selection components Query Generation for Multimedia Retrieval Multimedia Data Duplicate Removal Result Re-ranking
  7. 7. Answer Medium Selection • Classification – only text, – Text + image – text + video – text + image + video • Approach: – Question Based Classification – Answer Based Classification – Media Resource Analysis
  8. 8. Query Extraction For each QA pair, we generate three queries. 1. Convert the question to a query, 2. Identify several key concepts from verbose answer which will have the major impact on effectiveness. 3. Finally, we combine the two queries that are generated from the question and the answer respectively.
  9. 9. Query Generation for Multimedia Search • Query Extraction • The second step is query selection.
  10. 10. Query Selection • Three-class classification task, since we need to choose one from the three queries • We adopt the following features: – POS Histogram. • For the queries that contain a lot of complex verbs it will be difficult to retrieve meaningful multimedia results. • We use POS tagger to assign part-of-speech to each word of both question and answer. • Here we employ the Stanford Log-linear Part-Of-Speech Tagger and 36 POS are identified. • We then generate a 36-dimensional histogram, in which each bin counts the number of words belonging to the corresponding category of part-of-speech.
  11. 11. (2) Search performance prediction. – Clarity score for each query based on the KL divergence between the query and collection language models. – We can generate 6-dimensional search performance prediction features in all (there are three queries and search is performed on both image and video search engines). • Therefore, for each QA pair, we can generate 42- dimensional features. • Based on the extracted features, we train an SVM classifier with a labeled training set for classification • i.e., selecting one from the three queries.
  12. 12. Clarity function:
  13. 13. Multimedia Data Selection & Prediction • We perform search using the generated queries to collect image and video data with Google image and video search engines respectively. • Most of the current commercial search engines are built upon text-based indexing and usually return a lot of irrelevant results. • Therefore, – Re-ranking by exploring visual information is essential to reorder the initial text-based search results. – Here we adopt the graph-based re-ranking method.
  14. 14. Graph Based Re-Ranking & Duplicate Removal
  15. 15. List of Algorithms • Core sentence extraction from question • Stemming & stop-words removal on answers • Question Type based on Answer Medium ( Naïve Bayes) • Head word extraction • Media Resource Analysis – Clarity score based on KL Divergence • Query Generation • Query Selection – POS Feature Extraction – Search Performance Prediction • Multimedia Data selection & presentation – Graph based ranking – Face Detection Algorithms – Feature Extraction from images – Key frame identification & extraction
  16. 16. References 1. M. Surdeanu, M. Ciaramita, and H. Zaragoza, “Learning to rank answers on large online QA collections,” in Proc. Association for Computational Linguistics, 2008 2. S. Cronen-Townsend, Y. Zhou, andW. B. Croft, “Predicting query performance,” in Proc. ACM Int. SIGIR Conf., 2002. 3. Liqiang Nie, Meng Wang, Yue Gao, Zheng-Jun Zha, and Tat-Seng Chua “Beyond Text QA: Multimedia Answer Generation by Harvesting Web Information” IEEE Multimedia Transaction 2013
  17. 17. Thank You….

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