| other,21-1-E95-1021,ak | </term> , using <term> French </term> as our <term> | test language | </term> . We imposed a time limit on our | #27357 In this paper we compare two competing approaches to part-of-speech tagging, statistical and constraint-based disambiguation, using French as ourtest language. | |
| tech,12-4-E95-1021,ak | is reasonably good , comparable to <term> | taggers | </term> for <term> English </term> . But the <term> | #27421 The accuracy of the statistical method is reasonably good, comparable totaggers for English. | |
| tech,4-3-E95-1021,ak | model </term> . We describe the two <term> | systems | </term> and compare the results . The <term> | #27403 We describe the twosystems and compare the results. | |
| other,18-1-E95-1021,ak | constraint-based disambiguation </term> , using <term> | French | </term> as our <term> test language </term> . | #27354 In this paper we compare two competing approaches to part-of-speech tagging, statistical and constraint-based disambiguation, usingFrench as our test language. | |
| tech,12-1-E95-1021,ak | <term> part-of-speech tagging </term> , <term> | statistical and constraint-based disambiguation | </term> , using <term> French </term> as our <term> | #27348 In this paper we compare two competing approaches to part-of-speech tagging,statistical and constraint-based disambiguation, using French as our test language. | |
| tech,19-2-E95-1021,ak | of time spent on the design of our <term> | constraint system | </term> was about the same as the time we | #27379 We imposed a time limit on our experiment: the amount of time spent on the design of ourconstraint system was about the same as the time we used to train and test the easy-to-implement statistical model. | |
| other,17-5-E95-1021,ak | limited time we allowed ourselves for <term> | rule development | </term> . We provide a unified account of | #27442 But the constraint-based tagger seems to be superior even with the limited time we allowed ourselves forrule development. | |
| model,36-2-E95-1021,ak | train and test the easy-to-implement <term> | statistical model | </term> . We describe the two <term> systems | #27396 We imposed a time limit on our experiment: the amount of time spent on the design of our constraint system was about the same as the time we used to train and test the easy-to-implementstatistical model. | |
| measure(ment),1-4-E95-1021,ak | </term> and compare the results . The <term> | accuracy | </term> of the <term> statistical method </term> | #27410 Theaccuracy of the statistical method is reasonably good, comparable to taggers for English. | |
| tech,9-1-E95-1021,ak | compare two competing approaches to <term> | part-of-speech tagging | </term> , <term> statistical and constraint-based | #27345 In this paper we compare two competing approaches topart-of-speech tagging, statistical and constraint-based disambiguation, using French as our test language. | |
| other,14-4-E95-1021,ak | comparable to <term> taggers </term> for <term> | English | </term> . But the <term> constraint-based tagger | #27423 The accuracy of the statistical method is reasonably good, comparable to taggers forEnglish. | |
| tech,2-5-E95-1021,ak | </term> for <term> English </term> . But the <term> | constraint-based tagger | </term> seems to be superior even with the | #27427 But theconstraint-based tagger seems to be superior even with the limited time we allowed ourselves for rule development. | |
| tech,4-4-E95-1021,ak | results . The <term> accuracy </term> of the <term> | statistical method | </term> is reasonably good , comparable to | #27413 The accuracy of thestatistical method is reasonably good, comparable to taggers for English. |
