== 信息检索和自然语言处理 IR&NLP ==

书籍：

Natural Language Processing with Python
--- Analyzing Text with the Natural Language Toolkit

http://nltk.org/book/

PYTHON自然语言处理中文翻译 NLTK Natural Language Processing with Python 中文版.pdf

http://vdisk.weibo.com/s/4ffue/1334656530

资源：

NLTK 2.0 documentation

http://nltk.org/

Project Gutenberg - free ebooks

http://www.gutenberg.org/

Statistical natural language processing and corpus-based computational linguistics: An annotated list of resources

http://www-nlp.stanford.edu/links/statnlp.html

Machine Learning for Natural Language Processing (ML-for-NLP)

https://wiki.inf.ed.ac.uk/MLforNLP

Readings in information retrieval

http://dl.acm.org/citation.cfm?id=275537&picked=prox&cfid=174312975&cftoken=79150195

信息检索领域主要期刊和会议

http://blog.so8848.com/2012/09/blog-post_8.html

Information Retrieval Resources

http://nlp.stanford.edu/IR-book/information-retrieval.html

Textbooks and Readings

http://courses.ischool.berkeley.edu/i240/s07/readings.php

Power Searching

http://www.powersearchingwithgoogle.com/

搜索前沿

http://www.google.com/intl/zh-CN_ALL/insidesearch/

liulixiang.info的博客 - Lantern Paper Search

http://liulixiang.info/wiki/index.php?title=Paper_search

课程：

CS 276 / LING 286 Information Retrieval and Web Search Spring 2012

http://www.stanford.edu/class/cs276/index.html

CS769

University of Wisconsin–Madison

Jurafsky and Martin. Speech and Natural Language Processing 
Ch 6 (HMM and ME models) 
Ch 13,14,15 (Parsing) 
Ch 18,19,20 (Semantics) 
Ch 25 (MT)

Michael Collins. Discriminative training methods for hidden Markov models: Theory and experiments with perceptron algorithms. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1–8, Philadelphia, Pennsylvania, USA, July 2002.

Bernardo Merialdo. Tagging English text with a probabilistic model. Computational Linguistics, 20 (2):155–72, 1994.

Sharon Goldwater and Thomas L. Griffiths. A fully Bayesian approach to unsupervised part-of- speech tagging. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 744–751, Prague, Czech Republic, June 2007.

Andrew McCallum, Dayne Freitag, and Fernando Pereira. Maximum entropy Markov models for information extraction and segmentation. In Proceedings of the 17th International Conference on Machine Learning, pages 591–598, Stanford, California, USA, June–July 2000.

Michael Collins. Discriminative reranking for natural language parsing. In Proceedings of the 17th International Conference on Machine Learning, pages 175–182, Stanford, California, USA, June– July 2000.

Mark Johnson, Thomas L. Griffiths, and Sharon Goldwater. Bayesian inference for PCFGs via Markov chain Monte Carlo. In Proceedings of the Human Language Technologies Conference of the North American Chapter of the Association for Computational Linguistics, pages 139–146, Rochester, New York, USA, April 2007b.

Ryan McDonald, Koby Crammer, and Fernando Pereira. Online large-margin training of dependency parsers. In Proceedings of the 43rd Annual Meeting of the Association of Computational Lin- guistics, pages 91–98, Ann Arbor, Michigan, USA, June 2005a

Slav Petrov, Leon Barrett, Romain Thibaux, and Dan Klein. Learning accurate, compact, and interpretable tree annotation. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, pages 433–440, Sydney, Australia, July 2006.

David Chiang. A hierarchical phrase-based model for statistical machine translation. In Proceedings of the 43rd Annual Meeting of the Association of Computational Linguistics, pages 263–270, Ann Arbor, Michigan, USA, June 2005.

Charles Sutton and Andrew McCallum. An Introduction to Conditional Random Fields. http://arxiv.org/abs/1011.4088

Luke S. Zettlemoyer and Michael Collins. Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars. In Proceedings of the 21st Conference in Uncertainty in Artificial Intelligence, pages 658–666, Edinburgh, UK, July 2005

David Yarowsky and Grace Ngai. Inducing multilingual POS taggers and NP bracketers via robust projection across aligned corpora. In Second Meeting of the North American Chapter of the Asso- ciation for Computational Linguistics, pages 200–207, Pittsburgh, Pennsylvania, USA, June 2001.

Dan Roth and Wen-tau Yih. A linear programming formulation for global inference in natural language tasks. In Proceedings of the Eighth Conference on Computational Natural Language Learning, pages 1–8, Boston, Massachusetts, USA, May 2004.

Noah A. Smith and Jason Eisner. Contrastive estimation: Training log-linear models on unlabeled data. In Proceedings of the 43rd Annual Meeting of the Association of Computational Linguistics, pages 354–362, Ann Arbor, Michigan, USA, June 2005.

Rion Snow, Daniel Jurafsky, and Andrew Y. Ng. Semantic taxonomy induction from heterogenous evidence. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, pages 801–808, Sydney, Australia, July 2006.