'Unseen words' presentation slideshows

A memory-based learning-plus-inference approach to morphological analysis

A memory-based learning-plus-inference approach to morphological analysis. Antal van den Bosch With Walter Daelemans, Ton Weijters, Erwin Marsi, Abdelhadi Soudi, and Sander Canisius ILK / Language and Information Sciences Dept. Tilburg University, The Netherlands

528 views • 41 slides

A memory-based learning-plus-inference approach to morphological analysis

A memory-based learning-plus-inference approach to morphological analysis. Antal van den Bosch With Walter Daelemans, Ton Weijters, Erwin Marsi, Abdelhadi Soudi, and Sander Canisius ILK / Language and Information Sciences Dept. Tilburg University, The Netherlands

413 views • 41 slides

Hindi Parts-of-Speech Tagging & Chunking

Hindi Parts-of-Speech Tagging & Chunking. Baskaran S MSRI. What's in?. Why POS tagging & chunking? Approach Challenges Unseen tag sequences Unknown words Results Future work Conclusion. Intro & Motivation. POS. Parts-of-Speech Dionysius Thrax (ca 100 BC)

840 views • 32 slides

Language Models for TR

318 views • 24 slides

????? ???? ?????? - ????? ????? POS Tagging Algorithms

311 views • 23 slides

Three Basic Problems

322 views • 22 slides

Three Basic Problems

Three Basic Problems. Compute the probability of a text: P m (W 1, N ) Compute maximum probability tag sequence: arg max T 1, N P m (T 1, N | W 1, N ) Compute maximum likelihood model arg max m P m (W 1, N ). Notation. a ij = Estimate of P(t i ?t j )

267 views • 18 slides

Language Models for TR

Language Models for TR. (Lecture for CS410-CXZ Text Info Systems) Feb. 25, 2011 ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign. Text mining paper. Food nutrition paper. Text Generation with Unigram LM. (Unigram) Language Model ?

259 views • 17 slides