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Hits 41 – 55 of 55

41
The semi-supervised switchboard transcription project
In: http://melodi.ee.washington.edu/~bilmes/mypubs/subramanya2009-s3tp.pdf (2009)
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42
Statistical Translation with scarce resources: A South African case study
In: http://www.meraka.org.za/lwazi/publications/kato07statistical.pdf (2007)
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43
Query expansion using random walk models
In: http://www.cs.cmu.edu/~callan/Papers/cikm05-kct.pdf (2005)
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44
Automatic acquisition of semantic lexicons for information retrieval ; Acquisition automatique de lexiques sémantiques pour la recherche d'information
Claveau, Vincent. - : HAL CCSD, 2003
In: https://tel.archives-ouvertes.fr/tel-00524646 ; Interface homme-machine [cs.HC]. Université Rennes 1, 2003. Français (2003)
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45
Semi-Supervised Semantic Role Labeling: Approaching from an Unsupervised Perspective
In: http://aclweb.org/anthology/C/C12/C12-1161.pdf
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46
NLP lexicons: innovative constructions and usages for machines and humans
In: http://www.trojina.si/elex2011/Vsebine/proceedings/eLex2011-11.pdf
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47
Improving Supervised Sense Disambiguation with Web-Scale Selectors
In: http://aclweb.org/anthology/C/C12/C12-1148.pdf
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48
Dirichlet Process Mixture Models for Verb Clustering
In: http://www.cl.cam.ac.uk/users/alk23/vlachos08icml.pdf
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49
Co-Training of Conditional Random Fields for Segmenting Sequence Data
In: https://dspace.jaist.ac.jp/dspace/bitstream/10119/3906/1/20134.pdf
Abstract: This paper presents a semi-supervised co-training appr-oach for discriminative sequential learning models, such as conditional random fields (CRFs). In this framework, different CRF models are trained on an initial set of sequence data according different views. The bootstrap-ping process is performed by iteratively adding new reliably inferred data sequences to the training data sets of CRF models retraining them. Reliable data sequences are inferred from a huge set of unlabeled data by estima-ting entropy values of predicted labels at time positions in data sequences. The inference and re-train operations are repeated a number of times in order that each CRF model should gain as much useful evidence from unlab-eled data and the other CRF models as possible. The proposed method was tested on noun phrase chunking and achieved significant results.
Keyword: co-training; cond- itional random fields; semi-supervised learning; text labeling
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.625.3466
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50
The Semi-Supervised Switchboard Transcription Project
In: http://melodi.ee.washington.edu/~bilmes/mypubs/subramanya09-s3tp.pdf
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51
Semi-supervised Noun Compound Analysis with Edge and Span Features
In: http://aclweb.org/anthology/C/C12/C12-1117.pdf
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52
Multiview Semi-Supervised Learning for Ranking Multilingual Documents
In: http://eprints.pascal-network.org/archive/00009301/01/MltViewRanking_ecml11.pdf
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53
Sentiment Classification in Resource-Scarce Languages by using Label Propagation
In: http://www.aclweb.org/anthology-new/Y/Y11/Y11-1044.pdf
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54
Robust multilingual Named Entity Recognition with shallow semi-supervised features
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55
A Semi-supervised approach for gender identification
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