Matches in SemOpenAlex for { <https://semopenalex.org/work/W4381735828> ?p ?o ?g. }
Showing items 1 to 61 of
61
with 100 items per page.
- W4381735828 endingPage "337" @default.
- W4381735828 startingPage "326" @default.
- W4381735828 abstract "We propose, in this paper,Part-Of-Speech (POS) tagging system is proposed which based on Hidden Markov Model (HMM) for several languages. HMM is implemented using Viterbi algorithm on 8 languages; English, Hindi, Telugu, Bangla (Bengali), Marathi, Standard Chinese, Portuguese and Spanish. The data for these languages were taken from the freely available corpora: Brown, NPS-Chat, Indiana, Sinica, Floresta and CESS-ESP Corpora. HMM is the most learning method used in many NLP applications, especially POS tagging. HMM taggerwas implemented by other researchersfor a lot of languages, where each one take his mother tongue language.system testing is done by splitting each corpus to 99% training and 1% testing. This testis repeated for 10 times by changing the training and test data. The accuracies (average for all 10 tests) for English (using two tagsets of 40 tags and 472 tags), English (NPS corpus), Hindi, Telugu, Bangla or Bengali, Marathi, Standard Chinese, Portuguese (using two tagsets of 32 tags and 269 tags), and Spanish (using two tagsets of 14 tags and 289 tags) are (95.3%& 92.39%), 87.17%, 81.3%, 74.03%, 72.01%, 69.56%, 87.59%, (84.56%& 83.95%), and (94.26%& 92.08%) respectively.Several languages are taken for recording the limitations of HMM tagger on different languages as will be seen, I.e, the limitations of using one method on many different languages are recorded. Same corpus annotated with different tagsetsis taken for studying the effect of tagset’s size.Also two different corpora, for the same language, are taken. According to our knowledge, there isn’t study implemented HMM on such various cases as in our work.We provide an executable application for tagging all words in any sentence for any of the used 8 languages in our work. The unknown words (words not exist in the trained data) are manipulated by a simple method as Laplace smoothing." @default.
- W4381735828 created "2023-06-24" @default.
- W4381735828 creator A5001904394 @default.
- W4381735828 creator A5092242324 @default.
- W4381735828 creator A5092242325 @default.
- W4381735828 date "2015-02-01" @default.
- W4381735828 modified "2023-09-25" @default.
- W4381735828 title "HMM Based POS Tagging System for 8 Different Languages and Several Tagsets" @default.
- W4381735828 doi "https://doi.org/10.30684/etj.33.2b.17" @default.
- W4381735828 hasPublicationYear "2015" @default.
- W4381735828 type Work @default.
- W4381735828 citedByCount "0" @default.
- W4381735828 crossrefType "journal-article" @default.
- W4381735828 hasAuthorship W4381735828A5001904394 @default.
- W4381735828 hasAuthorship W4381735828A5092242324 @default.
- W4381735828 hasAuthorship W4381735828A5092242325 @default.
- W4381735828 hasBestOaLocation W43817358281 @default.
- W4381735828 hasConcept C138885662 @default.
- W4381735828 hasConcept C154945302 @default.
- W4381735828 hasConcept C19235068 @default.
- W4381735828 hasConcept C204321447 @default.
- W4381735828 hasConcept C23224414 @default.
- W4381735828 hasConcept C2776844415 @default.
- W4381735828 hasConcept C2778756302 @default.
- W4381735828 hasConcept C2779662586 @default.
- W4381735828 hasConcept C28490314 @default.
- W4381735828 hasConcept C41008148 @default.
- W4381735828 hasConcept C41895202 @default.
- W4381735828 hasConcept C519982507 @default.
- W4381735828 hasConceptScore W4381735828C138885662 @default.
- W4381735828 hasConceptScore W4381735828C154945302 @default.
- W4381735828 hasConceptScore W4381735828C19235068 @default.
- W4381735828 hasConceptScore W4381735828C204321447 @default.
- W4381735828 hasConceptScore W4381735828C23224414 @default.
- W4381735828 hasConceptScore W4381735828C2776844415 @default.
- W4381735828 hasConceptScore W4381735828C2778756302 @default.
- W4381735828 hasConceptScore W4381735828C2779662586 @default.
- W4381735828 hasConceptScore W4381735828C28490314 @default.
- W4381735828 hasConceptScore W4381735828C41008148 @default.
- W4381735828 hasConceptScore W4381735828C41895202 @default.
- W4381735828 hasConceptScore W4381735828C519982507 @default.
- W4381735828 hasIssue "2B" @default.
- W4381735828 hasLocation W43817358281 @default.
- W4381735828 hasOpenAccess W4381735828 @default.
- W4381735828 hasPrimaryLocation W43817358281 @default.
- W4381735828 hasRelatedWork W2544371205 @default.
- W4381735828 hasRelatedWork W2994651209 @default.
- W4381735828 hasRelatedWork W3028872947 @default.
- W4381735828 hasRelatedWork W3031586918 @default.
- W4381735828 hasRelatedWork W3043528814 @default.
- W4381735828 hasRelatedWork W3184477063 @default.
- W4381735828 hasRelatedWork W4226218748 @default.
- W4381735828 hasRelatedWork W4297712837 @default.
- W4381735828 hasRelatedWork W4297928567 @default.
- W4381735828 hasRelatedWork W4366999515 @default.
- W4381735828 hasVolume "33" @default.
- W4381735828 isParatext "false" @default.
- W4381735828 isRetracted "false" @default.
- W4381735828 workType "article" @default.