Matches in SemOpenAlex for { <https://semopenalex.org/work/W2350871731> ?p ?o ?g. }
Showing items 1 to 87 of
87
with 100 items per page.
- W2350871731 endingPage "668" @default.
- W2350871731 startingPage "654" @default.
- W2350871731 abstract "In this paper, we present an empirical study for improving the Korean text chunking based on machine learning and feature set selection approaches. We focus on two issues: the problem of selecting feature set for Korean chunking, and the problem of alleviating the data sparseness. To select a proper feature set, we use a heuristic method of searching through the space of feature sets using the estimated performance from a machine learning algorithm as a measure of incremental usefulness of a particular feature set. Besides, for smoothing the data sparseness, we suggest a method of using a general part-of-speech tag set and selective lexical information under the consideration of Korean language characteristics. Experimental results showed that chunk tags and lexical information within a given context window are important features and spacing unit information is less important than others, which are independent on the machine teaming techniques. Furthermore, using the selective lexical information gives not only a smoothing effect but also the reduction of the feature space than using all of lexical information. Korean text chunking based on the memory-based learning and the decision tree learning with the selected feature space showed the performance of precision/recall of 90.99%/92.52%, and 93.39%/93.41% respectively." @default.
- W2350871731 created "2016-06-24" @default.
- W2350871731 creator A5006643967 @default.
- W2350871731 creator A5043466383 @default.
- W2350871731 creator A5050829390 @default.
- W2350871731 creator A5062982950 @default.
- W2350871731 creator A5065549541 @default.
- W2350871731 date "2002-01-01" @default.
- W2350871731 modified "2023-09-26" @default.
- W2350871731 title "Improving the Performance of Korean Text Chunking by Machine learning Approaches based on Feature Set Selection" @default.
- W2350871731 hasPublicationYear "2002" @default.
- W2350871731 type Work @default.
- W2350871731 sameAs 2350871731 @default.
- W2350871731 citedByCount "0" @default.
- W2350871731 crossrefType "journal-article" @default.
- W2350871731 hasAuthorship W2350871731A5006643967 @default.
- W2350871731 hasAuthorship W2350871731A5043466383 @default.
- W2350871731 hasAuthorship W2350871731A5050829390 @default.
- W2350871731 hasAuthorship W2350871731A5062982950 @default.
- W2350871731 hasAuthorship W2350871731A5065549541 @default.
- W2350871731 hasConcept C111919701 @default.
- W2350871731 hasConcept C119857082 @default.
- W2350871731 hasConcept C127705205 @default.
- W2350871731 hasConcept C138885662 @default.
- W2350871731 hasConcept C148483581 @default.
- W2350871731 hasConcept C154945302 @default.
- W2350871731 hasConcept C173801870 @default.
- W2350871731 hasConcept C177264268 @default.
- W2350871731 hasConcept C199360897 @default.
- W2350871731 hasConcept C203357204 @default.
- W2350871731 hasConcept C204321447 @default.
- W2350871731 hasConcept C2776401178 @default.
- W2350871731 hasConcept C31972630 @default.
- W2350871731 hasConcept C3770464 @default.
- W2350871731 hasConcept C41008148 @default.
- W2350871731 hasConcept C41895202 @default.
- W2350871731 hasConcept C83665646 @default.
- W2350871731 hasConcept C84525736 @default.
- W2350871731 hasConceptScore W2350871731C111919701 @default.
- W2350871731 hasConceptScore W2350871731C119857082 @default.
- W2350871731 hasConceptScore W2350871731C127705205 @default.
- W2350871731 hasConceptScore W2350871731C138885662 @default.
- W2350871731 hasConceptScore W2350871731C148483581 @default.
- W2350871731 hasConceptScore W2350871731C154945302 @default.
- W2350871731 hasConceptScore W2350871731C173801870 @default.
- W2350871731 hasConceptScore W2350871731C177264268 @default.
- W2350871731 hasConceptScore W2350871731C199360897 @default.
- W2350871731 hasConceptScore W2350871731C203357204 @default.
- W2350871731 hasConceptScore W2350871731C204321447 @default.
- W2350871731 hasConceptScore W2350871731C2776401178 @default.
- W2350871731 hasConceptScore W2350871731C31972630 @default.
- W2350871731 hasConceptScore W2350871731C3770464 @default.
- W2350871731 hasConceptScore W2350871731C41008148 @default.
- W2350871731 hasConceptScore W2350871731C41895202 @default.
- W2350871731 hasConceptScore W2350871731C83665646 @default.
- W2350871731 hasConceptScore W2350871731C84525736 @default.
- W2350871731 hasIssue "9" @default.
- W2350871731 hasLocation W23508717311 @default.
- W2350871731 hasOpenAccess W2350871731 @default.
- W2350871731 hasPrimaryLocation W23508717311 @default.
- W2350871731 hasRelatedWork W121526379 @default.
- W2350871731 hasRelatedWork W1489348810 @default.
- W2350871731 hasRelatedWork W1553199198 @default.
- W2350871731 hasRelatedWork W1576941664 @default.
- W2350871731 hasRelatedWork W1634742235 @default.
- W2350871731 hasRelatedWork W1875993343 @default.
- W2350871731 hasRelatedWork W1976420489 @default.
- W2350871731 hasRelatedWork W1979374416 @default.
- W2350871731 hasRelatedWork W2105645651 @default.
- W2350871731 hasRelatedWork W2119405591 @default.
- W2350871731 hasRelatedWork W2123285588 @default.
- W2350871731 hasRelatedWork W2127357421 @default.
- W2350871731 hasRelatedWork W2133399553 @default.
- W2350871731 hasRelatedWork W2158256570 @default.
- W2350871731 hasRelatedWork W2381607216 @default.
- W2350871731 hasRelatedWork W2803483156 @default.
- W2350871731 hasRelatedWork W2962948877 @default.
- W2350871731 hasRelatedWork W3135146725 @default.
- W2350871731 hasRelatedWork W3160238138 @default.
- W2350871731 hasRelatedWork W2592297820 @default.
- W2350871731 hasVolume "29" @default.
- W2350871731 isParatext "false" @default.
- W2350871731 isRetracted "false" @default.
- W2350871731 magId "2350871731" @default.
- W2350871731 workType "article" @default.