---
res:
  bibo_abstract:
  - Many daily activities present information in the form of a stream of text, and
    often people can benefit from additional information on the topic discussed. TV
    broadcast news can be treated as one such stream of text; in this paper we discuss
    finding news articles on the web that are relevant to news currently being broadcast.We
    evaluated a variety of algorithms for this problem, looking at the impact of inverse
    document frequency, stemming, compounds, history, and query length on the relevance
    and coverage of news articles returned in real time during a broadcast. We also
    evaluated several postprocessing techniques for improving the precision, including
    reranking using additional terms, reranking by document similarity, and filtering
    on document similarity. For the best algorithm, 84%-91% of the articles found
    were relevant, with at least 64% of the articles being on the exact topic of the
    broadcast. In addition, a relevant article was found for at least 70% of the topics.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Monika H
      foaf_name: Henzinger, Monika H
      foaf_surname: Henzinger
      foaf_workInfoHomepage: http://www.librecat.org/personId=540c9bbd-f2de-11ec-812d-d04a5be85630
    orcid: 0000-0002-5008-6530
  - foaf_Person:
      foaf_givenName: Bay-Wei
      foaf_name: Chang, Bay-Wei
      foaf_surname: Chang
  - foaf_Person:
      foaf_givenName: Brian
      foaf_name: Milch, Brian
      foaf_surname: Milch
  - foaf_Person:
      foaf_givenName: Sergey
      foaf_name: Brin, Sergey
      foaf_surname: Brin
  bibo_doi: 10.1145/775152.775154
  dct_date: 2003^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/978-158113680-7
  dct_language: eng
  dct_publisher: Association for Computing Machinery@
  dct_title: Query-free news search@
...