Matches in SemOpenAlex for { <https://semopenalex.org/work/W2022978093> ?p ?o ?g. }
Showing items 1 to 61 of
61
with 100 items per page.
- W2022978093 abstract "Question answering systems use information retrieval (IR) and information extraction (IE) methods to retrieve documents containing a valid answer. Question classification plays an important role in the question answer frame to reduce the gap between question and answer. This paper presents our research work on automatic question classification through machine learning approaches. We have experimented with machine learning algorithms Support Vector Machines (SVM) using kernel methods. An effective way to integrate syntactic structures for question classification in machine learning algorithms is the use of tree kernel (TK) functions. Here we use SubTree kernel, SubSet Tree kernel with Bag of words and Partial Tree kernels. Trade-off between training error and margin, Costfactor and the decay factor has significant impact when we use SVM for the above mentioned kernel types. The experiments determined the individual impact for Trade-off between training error and margin, Cost-factor and the decay factor and later the combined effect for Trade-off between training error and margin, Cost-factor. For each kernel types depending on these result we also figure out some hyper planes which can maximize the performance. Based on some standard data set outcomes of our experiment for question classification is promising." @default.
- W2022978093 created "2016-06-24" @default.
- W2022978093 creator A5076670747 @default.
- W2022978093 date "2010-01-01" @default.
- W2022978093 modified "2023-10-18" @default.
- W2022978093 title "Performance Evaluation for Question Classification by Tree Kernels using Support Vector Machines" @default.
- W2022978093 cites W1522989131 @default.
- W2022978093 cites W1547701650 @default.
- W2022978093 cites W2051674731 @default.
- W2022978093 cites W2070246124 @default.
- W2022978093 cites W2086004682 @default.
- W2022978093 cites W2107425660 @default.
- W2022978093 cites W2107990343 @default.
- W2022978093 cites W2112706073 @default.
- W2022978093 cites W2131297983 @default.
- W2022978093 cites W2148603752 @default.
- W2022978093 cites W2164530124 @default.
- W2022978093 cites W2183055693 @default.
- W2022978093 doi "https://doi.org/10.4304/jcp.5.1.32-39" @default.
- W2022978093 hasPublicationYear "2010" @default.
- W2022978093 type Work @default.
- W2022978093 sameAs 2022978093 @default.
- W2022978093 citedByCount "2" @default.
- W2022978093 countsByYear W20229780932022 @default.
- W2022978093 crossrefType "journal-article" @default.
- W2022978093 hasAuthorship W2022978093A5076670747 @default.
- W2022978093 hasConcept C113174947 @default.
- W2022978093 hasConcept C114614502 @default.
- W2022978093 hasConcept C119857082 @default.
- W2022978093 hasConcept C12267149 @default.
- W2022978093 hasConcept C153180895 @default.
- W2022978093 hasConcept C154945302 @default.
- W2022978093 hasConcept C33923547 @default.
- W2022978093 hasConcept C41008148 @default.
- W2022978093 hasConceptScore W2022978093C113174947 @default.
- W2022978093 hasConceptScore W2022978093C114614502 @default.
- W2022978093 hasConceptScore W2022978093C119857082 @default.
- W2022978093 hasConceptScore W2022978093C12267149 @default.
- W2022978093 hasConceptScore W2022978093C153180895 @default.
- W2022978093 hasConceptScore W2022978093C154945302 @default.
- W2022978093 hasConceptScore W2022978093C33923547 @default.
- W2022978093 hasConceptScore W2022978093C41008148 @default.
- W2022978093 hasIssue "1" @default.
- W2022978093 hasLocation W20229780931 @default.
- W2022978093 hasOpenAccess W2022978093 @default.
- W2022978093 hasPrimaryLocation W20229780931 @default.
- W2022978093 hasRelatedWork W2041399278 @default.
- W2022978093 hasRelatedWork W2099369243 @default.
- W2022978093 hasRelatedWork W2120008580 @default.
- W2022978093 hasRelatedWork W2136184105 @default.
- W2022978093 hasRelatedWork W2163073107 @default.
- W2022978093 hasRelatedWork W3194539120 @default.
- W2022978093 hasRelatedWork W4205958290 @default.
- W2022978093 hasRelatedWork W4223656335 @default.
- W2022978093 hasRelatedWork W2187500075 @default.
- W2022978093 hasRelatedWork W2345184372 @default.
- W2022978093 hasVolume "5" @default.
- W2022978093 isParatext "false" @default.
- W2022978093 isRetracted "false" @default.
- W2022978093 magId "2022978093" @default.
- W2022978093 workType "article" @default.