| Introduction | This is done using a maximum entropy model (call it MAXENT). |
| Introduction | Then, the remaining constituents are ordered using a second maximum entropy model (MAXENTZ). |
| Introduction | The maximum entropy model for both steps rely on the following features: |
| Abstract | We use a maximum entropy classifier to predict translation errors by integrating word posterior probability feature and linguistic features. |
| Conclusions and Future Work | In this paper, we have presented a maximum entropy based approach to automatically detect errors in translation hypotheses generated by SMT |
| Error Detection with a Maximum Entropy Model | For classification, we employ the maximum entropy model (Berger et al., 1996) to predict whether a word 21) is correct or incorrect given its feature vector p. |
| Introduction | We integrate two sets of linguistic features into a maximum entropy (MaxEnt) model and develop a MaxEnt-based binary classifier to predict the category (correct or incorrect) for each word in a generated target sentence. |
| Automated Classification | We use a Maximum Entropy (Berger et al., 1996) classifier with a large number of boolean features, some of which are novel (e. g., the inclusion of words from WordNet definitions). |
| Automated Classification | Maximum Entropy classifiers have been effective on a variety of NLP problems including preposition sense disambiguation (Ye and Baldwin, 2007), which is somewhat similar to noun compound interpretation. |
| Automated Classification | The results for these runs using the Maximum Entropy classifier are presented in Table 4. |