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。 ---------------------------------------------------------------------- ☆☆☆ 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/ ----------------------------------------------------------------- To: sigmodj_lecture@tkl.iis.u-tokyo.ac.jp 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 ------------------------------------------------------------------ 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. -- ----------------------------------------------------------------------- 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