| Evaluations | Since our system predicts fine-grained clusters comparing against Freebase relations, the measure of recall is underestimated. |
| Evaluations | Since our systems predict more fine-grained clusters than |
| Introduction | fine-grained entity types of two arguments, to handle polysemy. |
| Introduction | It is difficult to discover a high-quality set of fine-grained entity types due to unknown criteria for developing such a set. |
| Introduction | In this paper we address the problem of polysemy, while we circumvent the problem of finding fine-grained entity types. |
| Related Work | They cluster arguments to fine-grained entity types and rank the associations of a relation with these entity types to discover selectional preferences. |
| Experiments and Results | Not surprisingly, the fine-grained performance is quite a bit lower than the core relations. |
| Learning Time Constraints | We also experiment with 7 fine-grained relations: |
| Learning Time Constraints | Obviously the more fine-grained a relation, the better it can inform a classifier. |
| Learning Time Constraints | We use a similar function for the seven fine-grained relations. |
| A Class-based Model of Agreement | After segmentation, we tag each segment with a fine-grained morpho-syntactic class. |
| Discussion of Translation Results | Finally, +POS+Agr shows the class-based model with the fine-grained classes (e. g., “Noun+Fem+S g”). |
| Experiments | For training the tagger, we automatically converted the ATE morphological analyses to the fine-grained class set. |
| Introduction | We address this shortcoming with an agreement model that scores sequences of fine-grained morpho-syntactic classes. |