Sentence examples for relation classification system from inspiring English sources

We will make use of this attribute in building our semantic relation classification system.

One feature encodes the relation type predicted by our ensemble-based medical semantic relation classification system.

Given that a corpus annotated with medical semantic relations exists, we adopt a corpus-based approach to building a medical semantic relation classification system.

Following the 2010 i2b2/VA evaluation scheme, a semantic relation classification system is evaluated on all but the 'no relation' types.

Since we are using the relation identification system to filter the no relation instances prior to relation classification, the performance of the downstream relation classification system depends to a large extent on the performance of the identification system.

> -wrap-foot> To gain additional insights into the errors made by the 12-class relation classification system and the relation identification system, we perform an error analysis of each of them.

First, we exploit a simple but rational way to specify which input tokens are the target nominals in the input sentence, instead of Position Feature that used in other neural network relation classification systems.

The creation of the TimeBank corpus (1), as well as the organization of the TempEval-1 (2) and TempEval-2 (3) evaluation exercises, has facilitated the evaluation of temporal relation classification systems for the news domain.

To build a strong baseline, we represent each instance using 167 features modeled after the top-performing temporal relation classification systems on TimeBank (e.g. 6 8) and the i2b2 corpus (e.g. 9, 10), as well as those in the TempEval shared tasks (e.g. 11 14).

Note that the parser-based systems and relation classification used the spreading strategies as described in Section 2.6.

Since the best-performing systems for temporal relation classification for both the news and clinical domains are learning-based, we will employ a learning-based system as our baseline.