Composite Semantic Relation Classification
This addresses a bottleneck in semantic interpretation for NLP applications, but it is incremental as it extends existing relation classification methods.
The paper tackles the problem of classifying composite semantic relations between entities in text, which is needed for tasks like text entailment and question answering, by proposing a model that combines a commonsense knowledge base, a distributional navigational algorithm, and sequence classification.
Different semantic interpretation tasks such as text entailment and question answering require the classification of semantic relations between terms or entities within text. However, in most cases it is not possible to assign a direct semantic relation between entities/terms. This paper proposes an approach for composite semantic relation classification, extending the traditional semantic relation classification task. Different from existing approaches, which use machine learning models built over lexical and distributional word vector features, the proposed model uses the combination of a large commonsense knowledge base of binary relations, a distributional navigational algorithm and sequence classification to provide a solution for the composite semantic relation classification problem.