label propagation based semisupervised learning

... In the la- bel propagation process, the soft label of each initial labeled example is clamped in each iteration to re- plenish label sources from these labeled data. Thus the labeled data act ... LP based relation extraction method can use the graph structure to smooth the labels of unlabeled examples. Therefore, the labels of unlabeled exam- ples are determined not only by the nearby labeled examples, ... same class would be as large as possible. This will make label propagation to stay within the same class. This label propagation process will make the labeling function smooth on the graph. Define an n...

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... server. Table 10.3: Samba File Share Options Option Group Option Description volume The volume label returned for the share. 10.3.2.3 Configuring printer share parameters You configure printer ... configuration changes, you can re-install Linux. However, you can spare yourself much inconvenience by learning how Linux stores its network configuration. As you'll see, by using a text editor ... change the hostname, be sure to specify only the hostname itself; do not 10. Setting Up a Linux -Based LAN This chapter explains how to set up a local-area network (LAN) that includes a Linux...

Ngày tải lên: 14/12/2013, 13:15

... system. pwd Displays the current working directory on the remote system. 12. Setting Up a Linux -Based WAN In the last chapter, you learned how to connect your Linux system to a local- area network...

Ngày tải lên: 14/12/2013, 13:15

... transformation -based learning is the readability of learned rules. A dis- advantage is that the rule extraction pro- cedure is time-consuming. We present incremental -based, transformation -based learning ... t type relation- ship. Transformation -based learning was proposed by (Brill, 1995). Below we explain our learn- ing strategy when we directly adapt the learning method to our graph expression ... statistical learning; however, such work is hard, especially for tasks containing semantic elements. This paper describes predi- cate argument structure analysis using   transformation -based learning. ...

Ngày tải lên: 20/02/2014, 04:20

... lim- ited amount of labeled data, i.e., correctly labeled (true/false entailment) sentences. Recent research indicates that using labeled and unlabeled data in semi-supervised learning (SSL) environment, ... SVM and our graph based SSL method with no summarization to learn models using labeled training and testing datasets. For SSL we used the training as labeled and testing as unlabeled dataset in ... graph-summarization method to enable learning from a very large unlabeled and rather small labeled data, which would not have been feasible for most sophisticated learning tools in section 4. Finally...

Ngày tải lên: 20/02/2014, 07:20

... This paper describes two methods, one based on rules and the other based on statistical learning, to predict the un- known time argument for an event by the propagation from its related events. ... and section 4.2 will present the statistical learning framework respectively. 4.1 Rule based Prediction The easiest solution is to encode rules based on constraints from event arguments and ... Statistical Learning based Prediction In addition, we take a more general statistical approach to capture the cross-event relations and predict unknown time arguments. We manually labeled some...

Ngày tải lên: 20/02/2014, 09:20

... involves first using Web -based method by Nagata et al., and if no translations are found on the Web, backing off to a hybrid method based on dictionary and Web -based statistics on words and ... answer key exactly, and 145 (48%) containing the answer key. 5 Conclusion We present a novel Web -based, data-intensive ap- proach to terminology translation from English to Mandarin Chinese. Experimental ... | ω ), for any given transliteration unit (TU) ω and transliteration char- acter (TC) τ . Based on the Expectation Maximiza- tion (EM) algorithm. A TU for an English name can be a syllable...

Ngày tải lên: 20/02/2014, 15:20

... tion: A Unifying View. Machine Learning 1, 1:47-80. Gfinter Neumann. 1994. Application of Explanation- based Learning for Efficient Processing of Constraint- based Grammars. In 10 th IEEE ... Explanation -Based Learning. In Proceed- ings of 7 ~h AAAI Conference, pages 564-569, Saint Paul, Minnesota. Tom M. Mitchell, Richard M. Keller, and Smadax T. Kedar-Carbelli. 1986. Explanation -Based ... Introduction In this paper we present some novel applications of the so-called Explanation -Based Learning technique (EBL) to parsing Lexicalized Tree-Adjoining gram- mars (LTAG). EBL techniques...

Ngày tải lên: 20/02/2014, 22:20

... 2009. c 2009 Association for Computational Linguistics An Alignment Algorithm using Belief Propagation and a Structure -Based Distortion Model Fabien Cromi ` eres Graduate school of informatics Kyoto University Kyoto, ... computationally ef- ficient procedure for an exact EM -based training. However, we will give some theoretical and empir- ical evidences that Loopy Belief Propagation can give us a good approximation procedure. Although ... canada. http://www.isi.edu/naturallanguage/download/hansard/. D. Gildea. 2003. Loosely tree -based alignment for machine translation. Proceedings of ACL, 3. T. Heskes. 2003. Stable fixed points of loopy be- lief propagation are minima of the bethe free...

Ngày tải lên: 22/02/2014, 02:20

... e -learning system is described in [7] which can adapt itself to its users and to the open web based on the usage of its learning materials. The system users are clustered based on their learning ... Academic Advising System. 4. Knowledge -based academic advising system framework in adaptive credit -based learning environment This section proposes a framework based on semantic technology and ... dynamically changing world [2]. On the basis of credit accumulation, Web -based learning systems are no longer closed learning Nguyen Thanh Binh et al. / VNU Journal of Science, Natural Sciences...

Ngày tải lên: 05/03/2014, 11:21

... matrix of soft label assign- ments, with ˆ Y vl representing the score of label l on node v. A graph -based SSL computes ˆ Y from {G, SY }. 2.2 Label Propagation (LP-ZGL) The label propagation method ... ∈ V is labeled. That is, S identifies the labeled nodes in the graph. C is the set of labels, with |C| = m representing the total number of labels. Y is the n × m matrix storing training label ... constraints in SSL. Because of the label propagation behavior, graph -based SSL algorithms assign classes to all nodes reachable in the graph from at least one of the labeled instance nodes. This allows...

Ngày tải lên: 07/03/2014, 22:20

... ambiguity has a negative im- pact on discriminative learning. We will continue working on this line of research and improve our discriminative learning model in the future, for ex- ample, by adding ... ambiguity a much more seri- ous issue. 4.2 Discriminative Learning Experiments To further study how spurious ambiguity affects the discriminative learning, we implemented a frame- work following Haghighi ... Stanford University. Philipp Koehn, Franz Josef Och, and Daniel Marcu. 2003. Statistical phrase -based translation. In HLT- NAACL. Shujie Liu, Chi-Ho Li, and Ming Zhou. 2010. Dis- criminative pruning...

Ngày tải lên: 07/03/2014, 22:20

... on WSJ section 23 in three settings: on unlabelled dependencies; on labelled dependencies with only bare labels (all functional tags discarded); on labelled dependencies with functional tags. Notice ... to labelled PARSEVAL with functional labels, the method would give around 87.8% performance (98.64% of the “usual” 89%) for Charniak’s parser. Adding functional labels can be viewed as a relabelling ... closer look at the evaluation re- sults. Obviously, relabelling does not change the unlabelled scores. The 1% improvement for eval- uation on bare labels suggests that our approach is capable not...

Ngày tải lên: 08/03/2014, 04:22