1 / 19

HYP Progress Update

HYP Progress Update. By Zhao Jin. Outline. Background Progress Update. Background. Query (Text-based) The set of keywords to be entered into the system to retrieve the desired information or resources Main category Traditional IR Web (ex. Google) OPAC (ex. LINC) Video (ex. TRECVID).

lana
Télécharger la présentation

HYP Progress Update

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. HYP Progress Update By Zhao Jin

  2. Outline • Background • Progress Update

  3. Background • Query (Text-based) • The set of keywords to be entered into the system to retrieve the desired information or resources • Main category • Traditional IR • Web (ex. Google) • OPAC (ex. LINC) • Video (ex. TRECVID)

  4. Background • Query Analysis • To analyze the pattern and hidden information in the queries • To efficiently classify and support such queries.

  5. Progress update • Mid-May to Early June • Background reading • Around 30 to 40 papers on various topic • Summarizing of key points in the paper

  6. Progress update • Mid-June to late-June • Log analysis • BBC Video Query • NUS OPAC Query • Background reading on OPAC and TRECVID

  7. Progress update • July to now • Follow up on two main topics • Query classification and division on content-based and feature-based keywords (OPAC) • Identifying ASR-oriented keywords in a video query (TRECVID) • Background reading on MARC, wordnet and LOC subject heading

  8. Progress update • Plan for the near future • Refine and experiment with the current ideas • Log analysis • Background reading (Textbook & Related paper) • Preparation for implementation

  9. Q&A?

  10. End of progress update • Thank you for your attention!

  11. Two types of keywords • Content-Based Keyword (CBK) • The keywords that concern what the item is about • Ex. title, subject heading, etc • Feature-Based Keyword (FBK) • The keywords that concern the features of the item. • Ex. author, publisher, genre, medium

  12. Benefits • Benefits: • Faster retrieval • More precise retrieval • Help in relevance ranking

  13. Possible implementation • Possible implementation: • term co-occurrence for concept division • list of special words and machine learning for FBK and CBK division • wordnet for classification among CBKs

  14. Possible implementation • Possible implementation: • CL and IL search algorithms for actual searching with CBKs. • list of special words and machine learning for classification among FBKs. • Marc record search algorithms for actual searching with FBKs. Back

  15. Means to retrieve shots • Example: • To find shots of “Bill Clinton” • Face recognition • Closed-caption • Automatic Speech Recognition (ASR)

  16. Metrics • Common VS Special (In reality) • How common in reality is the concept represented by the keyword. • Generic VS Specific • How generic is the concept represented by the keyword.

  17. Metrics • Concrete VS Abstract • Whether the keyword represented is concrete or abstract • Topic frequency (Low VS High) • How often the keyword becomes (closely related to) a topic.

  18. Metrics • Formal VS Informal • Whether the keyword is in formal or informal language • Written VS spoken • Whether the keyword is in spoken or written language

  19. Metrics • Feature-level VS Content-level • Whether the keyword is about the feature of the video (ex. camera motion) or the content of the video Back

More Related