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      <title-group>
        <article-title>Report from TREC-9</article-title>
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      <contrib-group>
        <contrib contrib-type="author">
          <string-name>TREC-</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Tracks</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Cross-language (English to Chinese)</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Filtering</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Interactive</string-name>
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        </contrib>
        <contrib contrib-type="author">
          <string-name>Query</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Question Answering</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Spoken Document Retrieval</string-name>
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        </contrib>
        <contrib contrib-type="author">
          <string-name>Relevance Judgments</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>Donna Harman, Ellen Voorhees Retrieval Group Information Access Division National Institute of Standards and Technology</institution>
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      <title>-</title>
      <p>• Judged highest priority mono- and
crosslingual run from each group
– 39 cross (75%) / 13 mono (25%)
– 51 auto / 1 manual (Thank you, Berkeley!)
• Added top 50 documents from each
judged run to the pool
• Mean actual pool size = 598 (39% of max)
within expected range
Answers, not documents</p>
      <p>Web searching</p>
      <p>Beyond text
Beyond just English
Human-in-the-loop</p>
      <p>Streamed text</p>
      <p>Static text
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001</p>
      <p>Cross Language Track
• Task: ad hoc search for documents
written in one language using topics in
another language
– 25 topics in English created by bilingual
assessors; Chinese version also available
– 126,937 documents; 188 MB in BIG5
– Hong Kong newspapers donated by Wiser Ltd.
• Hong Kong Commercial Data (Aug 98-Jul 99)
• Hong Kong Daily News (Feb 99-July 99)
• Takongnao (Oct 98-Mar 99)
% Contributions to Pool by Run
Type (Relevant documents)</p>
      <p>59
Monolingual</p>
    </sec>
    <sec id="sec-2">
      <title>BBN Technologies</title>
    </sec>
    <sec id="sec-3">
      <title>Fudan University</title>
      <p>IBM T.J. Watson Research Center
Johns Hopkins University
Korea Advanced Institute of Science and</p>
      <p>Technology
Microsoft Research, China
MNIS-TextWise Labs</p>
      <p>National Taiwan University
Resources: dictionaries/word lists
– LDC English - Mandarin word list
(~120,000 pairs)
– Chinese-English Translation</p>
      <p>Assistance (CETA) dictionary
– KingSoft online bilingual dictionary
– WordNet
– other local (proprietary)
dictionaries</p>
      <p>More participants
Queens College, CUNY</p>
    </sec>
    <sec id="sec-4">
      <title>RMIT University</title>
      <p>Telcordia Technologies, Inc.</p>
      <p>The Chinese University of Hong Kong</p>
    </sec>
    <sec id="sec-5">
      <title>Trans-EZ Inc. University of California at Berkeley University of Maryland University of Massachusetts</title>
      <p>Resources: software &amp; services
• MT
– HuaJian MT system
– IBM AlphaWorks translation server
– Alis Gist-in-Time MT system
• English analysis
– InXight LinguistX (English linguistic analysis)
– Apple Pie parser
– Brill’s POS tagger
• Chinese analysis/conversion
– Various Chinese segmenters (e.g., NMSU’s ch_seg)
– BIG5-&gt;GB converters (e.g., NJStar’s)
• Miscellaneous
– CMU’s WEAVER translation-pair extraction
– Yahoo search
English to Chinese Results
Cross-language vs. Monolingual
1
0.9
0.8
0.7
ino 0.6
is 0.5
c
re 0.4
P 0.3
0.2
0.1
0
BBN9XLA
msrcn1
fdut9xl2
CHUHK00XEC1
pir0XHxD
INQ7XL3
ibmcl9a
KAIST9xlqm
1
0.9
0.8
0.7
ino 0.6
is 0.5
c
re 0.4</p>
    </sec>
    <sec id="sec-6">
      <title>Crosslingual</title>
      <p>TREC 2001
• Cross language
– Chinese ? NTCIR workshop (NII, Japan)
– TREC task will be English, French? Arabic
• Filtering track using new Reuters corpus
• Interactive to investigate live web
• Expanded web and QA tracks
• New video track
What was learned from the</p>
      <p>Chinese CLIR track?
• Many approaches to English to Chinese
topic translation, including use of various
dictionaries, word lists, parallel text, and
commercial MT systems
• Extensive set of Chinese retrieval
experiments performed ranging from
various n-gram methods to word based to
complete language modeling
• Because of the tight focus of this track,
cross-system comparison is possible</p>
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