In this paper, we describe a system (CGSRC) for classifying four semantic relations: synonym, hypernym, antonym and meronym using convolutional neural networks (CNN). We have participated in CogALex-V semantic shared task of corpus-based identification of semantic relations. Proposed approach using CNN-based deep neural networks leveraging pre-compiled word2vec distributional neural embeddings ...

Classifying what-type questions into proper semantic categories is found more challenging than classifying other types in question answering systems. In this paper, we propose to classify what-type questions by head noun tagging. The approach highlights the role of head nouns as the category discriminator of whattype questions. To reduce the semantic ambiguities of head noun, we integrate local...

To overcome the limit of the conventional machine translation (MT) method based on compositional semantics, we proposed an Analogical Mapping (AM) method based on Semantic Typology and built a semantic category system for Japanese compound and complex sentences. The AM-method maps linguistic expressions into other expressions with the same meaning with semantic categorization (based on concepts...

This paper describes our system for SemEval-2010 Task 8 on multi-way classification of semantic relations between nominals. First, the type of semantic relation is classified. Then a relation typespecific classifier determines the relation direction. Classification is performed using SVM classifiers and a number of features that capture the context, semantic role affiliation, and possible pre-e...