PPT-Text Classification without Supervision: Incorporating World Knowledge and Domain Adaptation

Yangqiu Song Lane Department of CSEE West Virginia University 1 Much of the work was done at UIUC Collaborators Dan Roth Haixun Wang Shusen Wang Weizhu Chen 2 Text Categorization. John Blitzer. Shai Ben-David, Koby Crammer, Mark Dredze, Ryan McDonald, Fernando Pereira. Joint work with. Statistical models, multiple domains. Different Domains of Text. Huge variation in vocabulary & style. Generation and Adaptation. Some Notes. Each topic studied so far have a number of fielded applications. That is, they have been used in the “real world”. The topic of this lecture still has some outstanding research questions that need to be answered before we see large numbers of fielded applications . In-domain vs out-domain. Annotated data in. Domain A. A. Parser. Training. Parsing texts in . Domain A. Parsing texts in Domain B . In-domain. Out-domain. Motivation. F. ew or no labeled resources exist for parsing text of the target domain.. John Blitzer. Shai Ben-David, Koby Crammer, Mark Dredze, Ryan McDonald, Fernando Pereira. Joint work with. Statistical models, multiple domains. Different Domains of Text. Huge variation in vocabulary & style. Heterogeneous Face Recognition (HFR). Presenter: Yao-Hung. . Tsai. . . . . Dept.. . of. . Electrical. . Engineering,. . NTU. Oral Presentation:. 2014.05.02. Outline. Face Recognition. Under. : Prof. Amitabha Mukherjee. By. : Narendra Roy. Roll no. : 11451. Group. : 6. Published by. : . Himanshu Bhatt,. Deepali Semwal. Shourya Roy. Introduction. Supervised machine learning classifications assume both training and test data are sampled from same domain or distribution (. NIPS Adaptation Workshop. With thanks to: . Collaborators:. . Ming-Wei . Chang, . Michael Connor, Gourab Kundu, Alla Rozovskaya. Funding. : . NSF, MIAS-DHS, NIH, DARPA, ARL, DoE. Adaptation. without. IR . Lecture 3 of 5: . Patent IR. Mihai Lupu . lupu@ifs.tuwien.ac.at. Russian Summer School on Information Retrieval. August 22-26, 2016 Saratov, Russian Federation. Outline. Monolingual text. TF/IDF, document length, queries from documents, latent semantics, NLP. John Blitzer and Hal . Daumé. III. TexPoint. fonts used in EMF. . Read the . TexPoint. manual before you delete this box.: . A. A. A. A. A. A. A. A. A. Classical “Single-domain” Learning. Predict:. Alexander Fraser. CIS, LMU Munich. 2017-10-24 . WP1: Structured Prediction and Domain Adaptation. Outline. Introduction to structured prediction and domain adaptation. Review of very basic structured prediction. Please sit down if you:. Are taller than 5’9”. Have blonde Hair . Have brown Eyes. Are left-Handed. Why Classify?. To study the diversity of life, biologists use a . classification . system to name organisms and group them in a logical manner. Heterogeneous Information Networks. Yangqiu. . Song. 1. Collaborators. Chenguang. . Wang . Ming Zhang. . . Yizhou. Sun . . Jiawei. . Han . AndeanSouthern AfricaWest Asia/GCCHindu Kush HimalayanIndian Ocean North Africa For further information contact Lima Adaptation Knowledge Initiative: Closingknowledgegapsto scale adaptation Photo by T (* indicates equal contribution). Hao He*. Dina . Katabi. Hao . Wang. *. ICML 2020 Oral. Domain Adaptation. One to One. Source Domain. Target Domain. and. .  .  . Many to One. Single Target Domain.