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• Machine Translation
  ...in the form of text or speech, into another natural language with computer algorithms/software. * '''Statistical''', generates translations by using statistical methods on bilingual corpora
  2 KB (187 words) - 15:46, 30 November 2010
• Compare Leskovec et al. WWW 10 and Leskovec et al. WWW 08
  Empirical comparison of algorithms for network community detection, [[Leskovec et al., WWW 2010]] [[ Statistical properties of community structure in large social and information networks.
  2 KB (318 words) - 00:17, 6 November 2012
• Part of Speech Tagging
  ...den Markov Models''' based approaches, sometimes referred to as stochastic algorithms in older literature * '''Dynamic Programming''' - Viterbi-like algorithms by DeRose & Church, mentioned for historical reasons
  2 KB (309 words) - 02:32, 23 November 2010
• Zhang et all, WWW 2007
  ...Lada Adamic. 2007. Expertise networks in online communities: structure and algorithms. In Proceedings of the 16th international conference on World Wide Web (WWW ...ties. This [[AddressesProblem::expertise finding]] system uses graph-based algorithms on social networks within the community.
  4 KB (548 words) - 22:30, 1 April 2011
• Class Meeting for 10-710 10-11-2011
  ...w, in semiring weighted logic program notation, the CKY and Earley parsing algorithms * [http://www.ark.cs.cmu.edu/LS2/images/a/a4/Charniak1997.pdf Statistical Parsing with a Context-free Grammar and Word Statistics], E. Charniak, AAAI
  3 KB (473 words) - 17:59, 9 October 2011
• Berg-Kirkpatrick et al, ACL 2010: Painless Unsupervised Learning with Features
  ...nal probability distributions (CPDs) with miniature log-linear models. Two algorithms for unsupervised training of featurized HMMs are proposed. ...oses the concept of [[UsesMethod::Featurized_HMM|featurized HMMs]] and two algorithms for their unsupervised training. For a detailed elaboration, see the page [
  5 KB (590 words) - 14:37, 18 September 2011
• Class Meeting for 10-802 02/17/2011
  ...bitstream/handle/1/4645865/Goldenberg_NetSurvey.pdf?sequence=1 A survey of statistical network models], Goldenberg, Zheng, Fienberg, and Airoldi, Sections 1-3. ( * Leskovec, J., K. J Lang, and M. Mahoney. 2010. Empirical comparison of algorithms for network community detection. In Proceedings of the 19th international c
  2 KB (288 words) - 15:09, 14 March 2011
• Class Meeting for 10-802 10/09/2012
  ...bitstream/handle/1/4645865/Goldenberg_NetSurvey.pdf?sequence=1 A survey of statistical network models], Goldenberg, Zheng, Fienberg, and Airoldi, Sections 1-3. ( * Leskovec, J., K. J Lang, and M. Mahoney. 2010. Empirical comparison of algorithms for network community detection. In Proceedings of the 19th international c
  2 KB (313 words) - 12:40, 15 October 2012
• Leskovec et al., WWW 2010
  ...eskovec, Kevin J. Lang, and Michael Mahoney. 2010. Empirical comparison of algorithms for network community detection. In Proceedings of the 19th international c ...onnectivity than external connectivity, and then one applies approximation algorithms or heuristics to extract sets of nodes that are related to the objective fu
  6 KB (870 words) - 21:54, 26 September 2012
• Marcus and Wong, EMNLP 2002
  Marcu, D., & Wong, W. (2002). A phrase-based, joint probability model for statistical machine translation. In In Proceedings of EMNLP, pp. 133–139. ...[[Category::paper | work]] presents a phrase-to-phrase alignment model for Statistical [[AddressesProblem::Machine Translation]]. Alignment models are generally w
  4 KB (696 words) - 19:16, 26 November 2011
• Margin Infused Relaxed Algorithm
  ...oseph Keshet, Shai Shalev-Shwartz, Yoram Singer. Online Passive-Aggressive Algorithms. JMLR 7(Mar):551--585, 2006. | Crammer et al., 2006]]. A general definition of online training algorithms can be written down as follows:
  6 KB (926 words) - 17:44, 29 November 2011
• Gildea and Jurafsky Computational Linguistics 2002
  ...In addition, they did many various experiments to find out which features, algorithms, and techniques affect the performance of the system.
  3 KB (420 words) - 23:21, 30 November 2010
• Hopkins and May, EMNLP 2011. Tuning as Ranking
  This [[Category::paper]] presents a simple and scalable method for statistical machine translation parameter tuning based on the pairwise approach to ran ...nslation tasks and compared the performance of MERT, MIRA and PRO learning algorithms. They evaluated performance on phrase-based and syntax-based MT systems.
  8 KB (1,132 words) - 20:40, 29 November 2011
• HMM
  Hidden Markov Models (HMMs) are statistical models that are used for representing stochastic [http://en.wikipedia.org/w ...of observations. This is usually done using maximum likelihood estimation algorithms. The [http://en.wikipedia.org/wiki/Baum%E2%80%93Welch_algorithm Baum-Welch
  4 KB (614 words) - 15:20, 4 October 2011
• Community structure in social and biological networks
  A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide W ...ng the hierarchical clustering algorithm. However, hierarchical clustering algorithms require node distance (i.e, edge weight) as input. Various weighting method
  6 KB (875 words) - 00:11, 19 December 2012
• M. Hurst and K. Nigam. Retrieving topical sentiments from online document collection.
  ...pic. Here authors argue for the fusion of Topicality and Polarity by using statistical machine learning approaches to identify topics and shallow NLP techniques t * Tokenization followed by POS Tagging using a statistical tagger trained on PennTreebank Data.
  6 KB (882 words) - 08:38, 6 November 2012
• Belief Propagation
  Belief Propagation (BP) is a message passing inference method for statistical graphical models (e.g. Bayesian networks and Markov random fields). The bas ...omputationally hard problem. As a result, we need to find better inference algorithms to solve the above problems.
  6 KB (1,044 words) - 17:16, 13 October 2011
• Expectation–maximization algorithm
  ...ihood]] or [[maximum a posteriori]] (MAP) estimates of [[parameter]]s in [[statistical model]]s, where the model depends on unobserved [[latent variable]]s. The E |journal=[[Journal of the Royal Statistical Society]]. Series B (Methodological)
  39 KB (5,817 words) - 21:17, 26 September 2012
• Comparison Das et al WSDM 2011 and Zhao et al AAAI 2007
  Both papers made use of algorithms from time series models and graph clustering to solve their respective prob * [[RelatedPaper::Lin_et_al_KDD_2011|A Statistical Model for Popular Events Tracking in Social Communities. Lin et al, KDD 201
  5 KB (842 words) - 23:49, 5 November 2012
• DeNero et al, EMNLP 2008
  ...ted using the setup from the [http://www.statmt.org/wmt07/shared-task.html Statistical Machine Translation Workshop shared task], where the model is trained using ...of all possible phrase segmentations and alignments have been tested using algorithms such as [[Hill Climbing]] in
  6 KB (869 words) - 14:37, 13 October 2011

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