• Publications
    • Conference Papers
    • Workshop Papers
    • Journal Papers
    • Publicity
    • Books
    • Theses
    • Submitted
  • Professional Activities
  • Teaching
  • About
  • Contact

Edgar Meij

semantic search research ッ

  • Publications
    • Conference Papers
    • Workshop Papers
    • Journal Papers
    • Publicity
    • Books
    • Theses
    • Submitted
  • Professional Activities
  • Teaching
  • About
  • Contact

Dynamic query modeling for related content finding

06/08/2015 Blog Conference Papers Publications No Comments

While watching television, people increasingly consume additional content related to what they are watching. We consider the task of finding video content related to a live television broadcast for which we leverage the textual stream of subtitles associated with the broadcast. We model this task as a Markov decision process and propose a method that uses reinforcement learning to directly optimize the retrieval effectiveness of queries generated from the stream of subtitles. Our dynamic query modeling approach significantly outperforms state-of-the-art baselines for stationary query modeling and for text-based retrieval in a television setting. In particular we find that carefully weighting terms and decaying these weights based on recency significantly improves effectiveness. Moreover, our method is highly efficient and can be used in a live television setting, i.e., in near real time.

  • [PDF] D. Odijk, E. Meij, I. Sijaranamual, and M. de Rijke, “Dynamic query modeling for related content finding,” in SIGIR 2015: 38th international ACM SIGIR conference on Research and development in information retrieval, 2015.
    [Bibtex]
    @inproceedings{SIGIR:2015:Odijk,
    Author = {Odijk, Daan and Meij, Edgar and Sijaranamual, Isaac and de Rijke, Maarten},
    Booktitle = {{SIGIR 2015: 38th international ACM SIGIR conference on Research and development in information retrieval}},
    Date-Added = {2015-08-06 13:14:13 +0000},
    Date-Modified = {2015-08-06 13:39:24 +0000},
    Month = {August},
    Publisher = {ACM},
    Title = {Dynamic query modeling for related content finding},
    Year = {2015}}
Active learningdynamic-query-modeling-for-related-content-findingedgar-ie-is-watchingEntity findingEntity linkinghttpbit-ly1mppg7rhttpedgar-meij-prodynamic-query-modeling-related-content-findingInformation retrievalLanguage modelingMachine learningQuery modelingSemantic linking

Learning to Explain Entity Relationships in Knowledge Graphs

Mining, ranking and recommending entity aspects

Leave a Reply Cancel reply

Time limit is exhausted. Please reload CAPTCHA.

Edgar Meij logo

Welcome!

This is the website of Edgar Meij. I lead several groups of researchers and engineers at Bloomberg working on knowledge graphs, question answering, information retrieval, machine learning, and more…

Search

Tweets by @edgarmeij

Tags

AIDA Artificial Intelligence CLEF DBpedia Document priors edgar-meij entity-linking-and-retrieval entity-linking-and-retrieval-tutorial entity-linking-tutorial Entity finding Entity linking Information retrieval Knowledge base population Knowledge Graph Language modeling Linking Open Data LOD logo-penerbit-buku-internasional Lucene Machine learning meij MeSH Microblogs penerbit-buku-internasional Query log analysis Query modeling Relevance modeling Semanticizing Semantic linking Semantic query analysis Semantic search Teaching Text mining TREC Blog TREC Enterprise TREC Genomics TREC KBA TREC Microblog TREC Relevance Feedback Tutorial Twitter Web services Wikipedia Workflows Workshop
Proudly powered by WordPress | Theme: Doo by ThemeVS.