Matches in SemOpenAlex for { <https://semopenalex.org/work/W2071218019> ?p ?o ?g. }
Showing items 1 to 95 of
95
with 100 items per page.
- W2071218019 abstract "Rule based systems for processing text data encode the knowledge of a human expert into a rule base to take decisions based on interactions of the input data and the rule base. Similarly, supervised learning based systems can learn patterns present in a given dataset to make decisions on similar and other related data. Performances of both these classes of models are largely dependent on the training examples seen by them, based on which the learning was performed. Even though trained models might fit well on training data, the accuracies they yield on a new test data may be considerably different. Computing the accuracy of the learnt models on new unlabeled datasets is a challenging problem requiring costly labeling, and which is still likely to only cover a subset of the new data because of the large sizes of datasets involved. In this paper, we present a method to estimate the accuracy of a given model on a new dataset without manually labeling the data. We verify our method on large datasets for two shallow text processing tasks: document classification and postal address segmentation, and using both supervised machine learning methods and human generated rule based models." @default.
- W2071218019 created "2016-06-24" @default.
- W2071218019 creator A5004954473 @default.
- W2071218019 creator A5014398223 @default.
- W2071218019 creator A5041254552 @default.
- W2071218019 creator A5047987914 @default.
- W2071218019 date "2010-01-01" @default.
- W2071218019 modified "2023-09-23" @default.
- W2071218019 title "Estimating accuracy for text classification tasks on large unlabeled data" @default.
- W2071218019 cites W1479807131 @default.
- W2071218019 cites W1494580925 @default.
- W2071218019 cites W1519451279 @default.
- W2071218019 cites W1964357740 @default.
- W2071218019 cites W1995220928 @default.
- W2071218019 cites W1996956037 @default.
- W2071218019 cites W2038181716 @default.
- W2071218019 cites W2073251771 @default.
- W2071218019 cites W2113765418 @default.
- W2071218019 cites W2151023586 @default.
- W2071218019 cites W2156515921 @default.
- W2071218019 cites W2158188757 @default.
- W2071218019 cites W2160806787 @default.
- W2071218019 cites W2326325773 @default.
- W2071218019 cites W3103913776 @default.
- W2071218019 doi "https://doi.org/10.1145/1871437.1871551" @default.
- W2071218019 hasPublicationYear "2010" @default.
- W2071218019 type Work @default.
- W2071218019 sameAs 2071218019 @default.
- W2071218019 citedByCount "0" @default.
- W2071218019 crossrefType "proceedings-article" @default.
- W2071218019 hasAuthorship W2071218019A5004954473 @default.
- W2071218019 hasAuthorship W2071218019A5014398223 @default.
- W2071218019 hasAuthorship W2071218019A5041254552 @default.
- W2071218019 hasAuthorship W2071218019A5047987914 @default.
- W2071218019 hasConcept C104317684 @default.
- W2071218019 hasConcept C119857082 @default.
- W2071218019 hasConcept C124101348 @default.
- W2071218019 hasConcept C134306372 @default.
- W2071218019 hasConcept C136389625 @default.
- W2071218019 hasConcept C154945302 @default.
- W2071218019 hasConcept C16910744 @default.
- W2071218019 hasConcept C185592680 @default.
- W2071218019 hasConcept C199360897 @default.
- W2071218019 hasConcept C2776145971 @default.
- W2071218019 hasConcept C33923547 @default.
- W2071218019 hasConcept C41008148 @default.
- W2071218019 hasConcept C42058472 @default.
- W2071218019 hasConcept C4554734 @default.
- W2071218019 hasConcept C50644808 @default.
- W2071218019 hasConcept C55493867 @default.
- W2071218019 hasConcept C66746571 @default.
- W2071218019 hasConceptScore W2071218019C104317684 @default.
- W2071218019 hasConceptScore W2071218019C119857082 @default.
- W2071218019 hasConceptScore W2071218019C124101348 @default.
- W2071218019 hasConceptScore W2071218019C134306372 @default.
- W2071218019 hasConceptScore W2071218019C136389625 @default.
- W2071218019 hasConceptScore W2071218019C154945302 @default.
- W2071218019 hasConceptScore W2071218019C16910744 @default.
- W2071218019 hasConceptScore W2071218019C185592680 @default.
- W2071218019 hasConceptScore W2071218019C199360897 @default.
- W2071218019 hasConceptScore W2071218019C2776145971 @default.
- W2071218019 hasConceptScore W2071218019C33923547 @default.
- W2071218019 hasConceptScore W2071218019C41008148 @default.
- W2071218019 hasConceptScore W2071218019C42058472 @default.
- W2071218019 hasConceptScore W2071218019C4554734 @default.
- W2071218019 hasConceptScore W2071218019C50644808 @default.
- W2071218019 hasConceptScore W2071218019C55493867 @default.
- W2071218019 hasConceptScore W2071218019C66746571 @default.
- W2071218019 hasLocation W20712180191 @default.
- W2071218019 hasOpenAccess W2071218019 @default.
- W2071218019 hasPrimaryLocation W20712180191 @default.
- W2071218019 hasRelatedWork W1570930106 @default.
- W2071218019 hasRelatedWork W1579429723 @default.
- W2071218019 hasRelatedWork W1981301418 @default.
- W2071218019 hasRelatedWork W1997110747 @default.
- W2071218019 hasRelatedWork W2031180392 @default.
- W2071218019 hasRelatedWork W2098762642 @default.
- W2071218019 hasRelatedWork W2109946528 @default.
- W2071218019 hasRelatedWork W2159032819 @default.
- W2071218019 hasRelatedWork W2161330515 @default.
- W2071218019 hasRelatedWork W2591922431 @default.
- W2071218019 hasRelatedWork W2783366437 @default.
- W2071218019 hasRelatedWork W2885501967 @default.
- W2071218019 hasRelatedWork W2912330274 @default.
- W2071218019 hasRelatedWork W3049085523 @default.
- W2071218019 hasRelatedWork W3090756915 @default.
- W2071218019 hasRelatedWork W3107606673 @default.
- W2071218019 hasRelatedWork W3127344029 @default.
- W2071218019 hasRelatedWork W3160112926 @default.
- W2071218019 hasRelatedWork W3164560392 @default.
- W2071218019 hasRelatedWork W970023169 @default.
- W2071218019 isParatext "false" @default.
- W2071218019 isRetracted "false" @default.
- W2071218019 magId "2071218019" @default.
- W2071218019 workType "article" @default.