... a method of activelearning for WSD with pseudo negative examples, which are selected fromunlabeleddata by a classifier trained with positiveandunlabeled examples. McCallum and Nigam (1998) ... 61–64,Suntec, Singapore, 4 August 2009.c2009 ACL and AFNLPA Combination of ActiveLearningand Semi-supervised Learning Starting with PositiveandUnlabeled Examples for Word Sense Disambiguation: ... motivated us to study active learning for WSD starting with only positive ex-amples. The previous techniques (Chan and Ng, 2007; Chen et al. 2006) require balanced positive and negative examples...
... the labeled dataand the unlabeled data. Then we build a pseudo reference set for the unlabeled data, and calculate the error rate of each word aligner using only the labeled data. Based ... alignment as a case study. 5.1 Data We have two kinds of training datafrom general domain: Labeled Data (LD) andUnlabeledData (UD). The Chinese sentences in the data are automatically segmented ... labeled data and large amounts of unlabeled data. The proposed approach modifies the super-vised boosting algorithm to a semi-supervised learning algorithm by incor-porating the unlabeled data. ...
... at RAND oversaw data management for the project including obtaining and processing military personnel records, writing analysis programs, and facilitating data transfer to andfrom DMDC and ... discrepancies between survey and administrative data include Goldman and Smith (2001), Denmead and Turek (2005), Hurd and Rohwedder (2006), Kapteyn and Ypma (2007), and Haider and Loughran (2008). ... Base Year and Out Year and Active- Duty Days Served in the Out Year 37 A.2. Gross and Net Earnings Differences, by Base and Out Year 39 A.3. Gross and Net Earnings Losses, by Base and Out Year...
... classifier with robustness from noisy data (Ko and Seo, 2004). How can labeled training data be automatically created fromunlabeleddataand title words? Maybe unlabeleddata don’t have any information ... A. McCallum, S. Thrun, and T. Mitchell, 1998, Learning to Classify Text from Labeled and Unlabeled Documents, In Proc. of AAAI-98. K. P. Nigam, 2001, Using UnlabeledData to Improve Text Classification, ... related works, we presented two approaches using unlabeleddata in text categorization; one approach combines unlabeleddataand labeled data, and the other approach uses the clustering technique...
... (for training RL) from suchlimited data. The use of WOZ data has earlier been proposedin the context of RL. (Williams and Young, 2004)utilise WOZ data to discover the state and actionspace ... using data- driven methods. The em-ployed database contains 438 items and is similar inretrieval ambiguity and structure to the one used inthe WOZ experiment. The dialogue system used for learning ... FP6project “TALK: Talk and Look, Tools for Am-bient Linguistic Knowledge (IST 507802, www.talk-project.org), from the EPSRC, projectno. EP/E019501/1, andfrom the IRTG SaarlandUniversity.645...
... Country " + " ;FROM Customers"; SqlDataAdapter mySqlDataAdapter = new SqlDataAdapter(); mySqlDataAdapter.SelectCommand = mySqlCommand; DataSet myDataSet = new DataSet(); mySqlConnection.Open(); ... System .Data. SqlClient; class AddModifyAndRemoveDataRowViews { public static void DisplayDataRow( DataRow myDataRow, DataTable myDataTable ) { Console.WriteLine("\nIn DisplayDataRow()"); ... ADDMODIFYANDREMOVEDATAROWVIEWS.CS /* AddModifyAndRemoveDataRowViews.cs illustrates how to add, modify, and remove DataRowView objects from a DataView */ using System; using System .Data; ...
... MCR I and MCR II from Methanothermobacter marburgensis,MCR I from Methanocaldococcus jannaschii and Methanoculleus thermophi-lus, and MCR from Methanococcus voltae, Methanopyrus kandleri and Methanosarcina ... isoen-zyme I from Methanothermobacter marburgensis , MCR from Methanosarcina barkeri,andMCRfromMethano-pyrus kandleri [7]. In case of the enzyme from Methano-thermobacter marburgensis and Methanosarcina ... isoenzymes from Methanothermobacter mar-burgensis and in the enzymes from Methanococcus vol-tae, Methanoculleus thermophilus and Methanosarcinabarkeri, but is not methylated in the enzyme from Met-hanopyrus...
... a ‘gold standard’ humanutterance from our dataset, which they must com-pare with utterances generated by models trainedwith and without activelearning on a set of 20, 40,100, and 362 utterances ... initial dataset.As far as the learning method is concerned, apaired t-test shows that models trained on 20 and 40 utterances using activelearning significantlyoutperform models trained using random ... random sampling and active learning. Differences for training setsizes of 20 and 40 are all significant (p < .05).6 Related workWhile most previous work on trainable NLG re-lies on a handcrafted...
... graduated from college (COLLEGE); (8) the logarithm of the sum of the value of financial assets (bank and postal deposits and investment securities) and the value of real assets (land and housing) ... and X are economic and demographic household characteristics that affect loan supply and demand, and E are three dummy variables (1st Enforcement Quartile, 2nd Enforcement Quartile, and ... 125, 150, and 112 in 2003, 2004, 2005, 2006, and 2007, respectively. There are three advantages to using datafrom the JPSC. The biggest advantage of using the JPSC data is that this data set...
... RECOGNITION IN VIDEOS BY LEARNINGFROM WEB DATA 1679 Visual Event Recognition in Videosby Learningfrom Web Data Lixin Duan, Dong Xu, Member, IEEE, Ivor Wai-Hung Tsang, and Jiebo Luo, Fellow, ... samples) from the target domain and auxiliary domain are used tocalculate h in (6). Note that all test samples are used as unlabeled data during the learning process.Table 3 reports the means and ... observe that the absolute values of 1 and 2are alwaysDUAN ET AL.: VISUAL EVENT RECOGNITION IN VIDEOS BY LEARNINGFROM WEB DATA 1675TABLE 3Means and Standard Deviations (Percent) of MAPs over...
... 84.3dpo3 (Grand+Sib) 93.21 44.8 89.6 86.9dpo3 +Unlabeled (Edges) 93.12 43.6 85.3 87.0dpo3 +Unlabeled (Sib) 93.15 43.7 85.5 86.8dpo3 +Unlabeled (Grand) 93.55 46.1 90.6 87.5dpo3 +Unlabeled (Grand+Sib) ... may not add some additional gains.4 Using UnlabeledData EffectivelyAssociations fromunlabeleddata have the poten-tial to improve both conjunctions and prepositions.We predict that web counts ... the data representation and the learning representation both capture relevant prop-erties of prepositions and conjunctions. We predictthat Conversion 2 and a factorization which includesgrand-parent...
... and use of land were shared with the temples and with those members of the nobility closest tothe ruling monarch. Hence there were state lands and state income and temple lands and temple income. ... animals and working the land, could releasea comparatively large part of the population to devote its time and energy to trade and commerce, to industry and transport, to the arts and sciences and ... bymerchants and bankers who owned it and used it for their purposes. Accumulating wealth and money enabledthe traders, merchants, bankers and manufacturers to out-buy and out-point landlords and churchmen.Politically,...
... 1G-word unlabeled data 93.66 89.36 37M-word unlabeled data (Ando and Zhang, 2005) 93.15 89.31 27M-word unlabeled data (Florian et al., 2003) 93.87 88.76 own large gazetteers,2M-word labeled data (Suzuki ... PTB III data evaluated by label accuracysystem test additional resourcesJESS-CM (CRF/HMM) 95.15 1G-word unlabeled data 94.67 15M-word unlabeled data (Ando and Zhang, 2005) 94.39 15M-word unlabeled ... Results for POS tagging (PTB III data) , syntactic chunking (CoNLL’00 data) , and NER (CoNLL’03 data) incorporated with 1G-words of unlabeled data, and the performance gain from supervised CRFᎀ፵፸ᎀ፵፹ᎀ፵፺ᎀ፵፻፷...
... col-lect datafrom several open issue trackers, use theminimal amount of simple preprocessing and fil-ter heuristics to get useful input data, and publiclyshare both the raw and preprocessed data. We ... situation.4.1 Progressive validationWhen learningfromdata streams the standardevaluation methodology where data is split into aseparate training and test set is not applicable. Anevaluation ... stream forexploratory data analysis and feature and param-eter tuning, and then use progressive validation toevaluate on entirely unseen test data. Below wespecify the size and number of unique...
... thatunfold in the oral history. The themes of promise and obligation, loss and abandonmentguilt, poverty and survival, ritual and sacrifice, and pride and respect,spoken through the voice of a Chinese ... culture and the connections with family and community promote resiliency and happiness. At the same time, the obligations of family prescribed by cul-ture and guilt over abandoning family and culture ... areabandoned and shamed. These stories mimic Asian values and are writ-ten from Western perspectives. Westerners often fail to understand thatthe latter portrayals victimize Asian women and...