Learning to match names across languages

Mani, Inderjeet; Yeh, Alexander; Condon, Sherri L.

doi:10.3115/1613172.1613176

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Another type of cost-sensitive learning is the weighting method which applies a weight on the training instance that represents its misclassification cost. Such modifications have been made to common classification algorithms like the proximity-based classifiers [50], Support Vector Machines (SVMs) [51], decision trees [52], [53], and rule-based classifiers [54].…”

Section: Supervised Learningmentioning

confidence: 99%

“…Time series analysis [40], [41], [42] Statistical Non-parametric [3], [14], [19], [20] Signal processing [43], [44] Parametric [3], [14], [19], [20] Information theory [47], [48], [3] Spectral [45], [46], [3] Data-driven techniques Supervised [49], [50], [51], [52], [53], [54] Deep RBM [80], [56], [81], [82], [83] Semi-supervised [55], [56], [57], [58] CNN [68],[69] Unsupervised [59], [60], [61] GAN [77], [78], [79], [74] Reinforcement [62], [63], [64], [65], [66] Autoencoder [71], …”

Section: Conventional Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Smart anomaly detection in sensor systems: A multi-perspective review

Erhan

Ndubuaku²,

Mauro³

et al. 2021

Information Fusion

193

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Section: Supervised Learningmentioning

confidence: 99%

Section: Conventional Techniquesmentioning

confidence: 99%

Smart anomaly detection in sensor systems: A multi-perspective review

Erhan

Ndubuaku²,

Mauro³

et al. 2021

Information Fusion

193

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“…The most common methods in the data level are random oversampling [17] and random undersampling [27] . The former randomly duplicates the minority class samples and the latter randomly reduces the majority class samples to rebalance the dataset.…”

Section: Introductionmentioning

confidence: 99%

Joint Sample Position Based Noise Filtering and Mean Shift Clustering for Imbalanced Classification Learning

Duan,

Xue,

Huang

et al. 2024

Tsinghua Sci. Technol.

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The problem of imbalanced data classification learning has received much attention. Conventional classification algorithms are susceptible to data skew to favor majority samples and ignore minority samples. Majority weighted minority oversampling technique (MWMOTE) is an effective approach to solve this problem, however, it may suffer from the shortcomings of inadequate noise filtering and synthesizing the same samples as the original minority data. To this end, we propose an improved MWMOTE method named joint sample position based noise filtering and mean shift clustering (SPMSC) to solve these problems. Firstly, in order to effectively eliminate the effect of noisy samples, SPMSC uses a new noise filtering mechanism to determine whether a minority sample is noisy or not based on its position and distribution relative to the majority sample. Note that MWMOTE may generate duplicate samples, we then employ the mean shift algorithm to cluster minority samples to reduce synthetic replicate samples. Finally, data cleaning is performed on the processed data to further eliminate class overlap. Experiments on extensive benchmark datasets demonstrate the effectiveness of SPMSC compared with other sampling methods.

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Cross‐language person‐entity linking from 20 languages

Lawrie

Mayfield

McNamee

et al. 2014

Asso for Info Science & Tech

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The goal of entity linking is to associate references to an entity that is found in unstructured natural language content to an authoritative inventory of known entities. This article describes the construction of 6 test collections for cross-language person-entity linking that together span 22 languages. Fully automated components were used together with 2 crowdsourced validation stages to affordably generate ground-truth annotations with an accuracy comparable to that of a completely manual process. The resulting test collections each contain between 642 (Arabic) and 2,361 (Romanian) person references in non-English texts for which the correct resolution in English Wikipedia is known, plus a similar number of references for which no correct resolution into English Wikipedia is believed to exist. Fully automated cross-language person-name linking experiments with 20 non-English languages yielded a resolution accuracy of between 0.84 (Serbian) and 0.98 (Romanian), which compares favorably with previously reported cross-language entity linking results for Spanish.

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Learning to match names across languages

Cited by 5 publications

References 3 publications

Smart anomaly detection in sensor systems: A multi-perspective review

Smart anomaly detection in sensor systems: A multi-perspective review

Joint Sample Position Based Noise Filtering and Mean Shift Clustering for Imbalanced Classification Learning

Cross‐language person‐entity linking from 20 languages

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