| A00-1034 | information based on MaxEnt . Using | MaxEnt | , the system may learn under |
| A00-1034 | contextual information based on | MaxEnt | . Using MaxEnt , the system may |
| A00-1034 | perfect gazetteers . For this , a | MaxEnt | approach works well in utilizing |
| A00-1034 | combining gazetteer information using | MaxEnt | . The constrained HMM is described |
| A00-1034 | a very high precision tagger . | MaxEnt | includes external gazetteers |
| A00-1034 | back-off models used in HMMs , | MaxEnt | provides a systematic method |
| A00-1034 | in a black box fashion by using | MaxEnt | . They demonstrate the superior |
| D08-1013 | better than Naive Bayes , SVM and | MaxEnt | methods . We think that is because |
| A00-1034 | discusses sub-type generation by | MaxEnt | . The experimental results and |
| A00-1034 | location and organization . Based on | MaxEnt | , the last module derives sub-categories |
| D08-1013 | linear kernel function4 . For | MaxEnt | , we use the implementation in |
| A00-1034 | error prone , this module employs | MaxEnt | to build a statistical model |
| D08-1013 | traditional methods , such as NB , SVM , | MaxEnt | , etc. . But the performance |
| A00-1034 | -RSB- and Maximum Entropy Models ( | MaxEnt | ) -LSB- Borthwick 1998 -RSB- |
| D08-1060 | were first word-aligned using a | MaxEnt | aligner ( Ittycheriah and Roukos |
| A00-1034 | symbolic and statistical approaches . | MaxEnt | has been demonstrated to be a |
| A00-1034 | for sub-categorization , since | MaxEnt | is powerful enough to incorporate |
| A00-2013 | Since the public version of the | MaxEnt | tagger can not be modified to |
| A00-1034 | , the result from the current | MaxEnt | module is not sufficiently accurate |
| C02-1127 | Clinton , Jordan , etc. . Using | MaxEnt | , systems learn under what situation |
