日本データベース学会の皆様、
ACM　SIGMOD　Japanの皆様、

東大生研の中野です。

１２月７日（水）の講演会のご案内をいたします。
奮ってご参加ください。

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　☆☆☆　　Dr. Jeffrey Xu Yu　講演会のご案内　　　☆☆☆

共催　日本データベース学会
　　　ACM SIGMOD日本支部

日時　１２月７日（水）　午前１０時３０分〜午前１１時３０分
場所　東京大学生産技術研究所　E棟 会議室A(Ew-501)
      E棟　５階　エレベータをあがってすぐ
      http://www.iis.u-tokyo.ac.jp/map/index.html

Title:  Text Classification without Labeled Negative Documents
Speaker: Prof. Jeffrey　Xu YU（Chinese University of Hong Kong)

参加費　無料

参加ご希望の方は、
　１．日本データベース学会のホームページにて
　　　( http://www.dbsj.org/ )
　２．ＳＩＧＭＯＤホームページにて
     （ http://www.sigmodj.org/ ）
　　　のいずれかより、
　　　会員登録の後（会費無料、すでに登録されている方は結構です）、
      sigmodj_lecture@tkl.iis.u-tokyo.ac.jpに
　　　添付の参加申込書をお送り下さい。

皆様のご参加をお待ちしております。

                        ACM　SIGMOD日本支部　支部長　北川博之
　　　　　　　　　　　　　　　　　　　　　　 担当幹事　中野　美由紀

                        連絡(問合せ)先　ACM SIGMOD日本支部
                                sigmodj_lecture@tkl.iis.u-tokyo.ac.jp
　　　　　　　　　　　　　　　　http://www.sigmodj.org/



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ACM SIGMOD日本支部　講演会　参加申し込み

１２月７日（水）の講演会に参加
・名前　　　
・ご所属
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Title:  Text Classification without Labeled Negative Documents
Speaker: Prof. Jeffrey　Xu YU（Chinese University of Hong Kong)


This talk presents a new solution for the problem of building a text
classifier with a small set of labeled positive documents (P) and a
large set of unlabeled documents (U). Here, the unlabeled documents
are mixed with both of the positive and negative documents. In other
words, no document is labeled as negative. This makes the task of
building a reliable text classifier challenging. In general, the
existing approaches use a two-step approach: i) extract the negative
documents (N) from U; and ii) build a classifier based on P and
N. However, none of the reported studies tries to further extract
any positive documents from U. In fact, intuitively, extracting
positive documents from U will increase the reliability of the
classifier. However, extracting positive documents from U is
difficult. A document in U that possesses the features exhibited in
P does not necessarily mean that it is a positive document, and vice
versa.
It is very sensitive to extract positive documents, because those
extracted positive samples may become noises.
The very large size of U and very high diversity exhibited there
also contribute to the difficulty of extracting any positive
documents. Thus, we propose a partition-based heuristic which
aims at extracting both the positive and negative documents in
U. Extensive experiments based on three benchmarks are
conducted. The favorable results indicated that our proposed heuristic
outperforms all of the existing approaches significantly, especially
in the case where the size of P is extremely small.
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中野　美由紀 東京大学　生産技術研究所　喜連川研究室 
Miyuki NAKANO Institute of Industrial Science, Univ. of Tokyo 
miyuki@tkl.iis.u-tokyo.ac.jp