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Hits 41 – 55 of 55
41
The semi-supervised switchboard transcription project
Amarnag Subramanya
;
Jeff Bilmes
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
R. S. M. Kato
;
E. Barnard
In: http://www.meraka.org.za/lwazi/publications/kato07statistical.pdf (2007)
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43
Query expansion using random walk models
Kevyn Collins-thompson
;
Jamie Callan
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
Nuria Gala
;
Mathieu Lafourcade
In: http://www.trojina.si/elex2011/Vsebine/proceedings/eLex2011-11.pdf
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47
Improving Supervised Sense Disambiguation with Web-Scale Selectors
H.
;
Schwar Fern
In: http://aclweb.org/anthology/C/C12/C12-1148.pdf
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48
Dirichlet Process Mixture Models for Verb Clustering
Andreas Vlachos
;
Zoubin Ghahramani
;
Anna Korhonen
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
Xuan-hieu Phan
;
Le-minh Nguyen
;
Yasushi Inoguchi
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
Amarnag Subramanya
;
Jeff Bilmes
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
Nicolas Usunier
;
Massih-reza Amini
;
Cyril Goutte
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
Yong Ren A
;
Nobuhiro Kaji B
;
Naoki Yoshinaga B
...
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
Agerri, Rodrigo
;
Rigau Claramunt, German
. - : Elsevier
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55
A Semi-supervised approach for gender identification
Soler Company, Juan
;
Wanner, Leo
. - : LREC
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