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    <abstract>This paper describes our system submission to the CogALex-2016 Shared Task on Corpus-Based Identification of Semantic Relations. Our system won first place for Task-1 and second place for Task-2. The evaluation results of our system on the test set is 88.1% (79.0% for TRUE only) f-measure for Task-1 on detecting semantic similarity, and 76.0% (42.3% when excluding RANDOM) for Task-2 on identifying finer-grained semantic relations. In our experiments, we try word analogy, linear regression, and multi-task Convolutional Neural Networks (CNNs) with word embeddings from publicly available word vectors. We found that linear regression performs better in the binary classification (Task-1), while CNNs have better performance in the multi-class semantic classification (Task-2). We assume that word analogy is more suited for deterministic answers rather than handling the ambiguity of one-to-many and many-to-many relationships. We also show that classifier performance could benefit from balancing the distribution of labels in the training data.</abstract>
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%0 Conference Proceedings
%T CogALex-V Shared Task: GHHH - Detecting Semantic Relations via Word Embeddings
%A Attia, Mohammed
%A Maharjan, Suraj
%A Samih, Younes
%A Kallmeyer, Laura
%A Solorio, Thamar
%Y Zock, Michael
%Y Lenci, Alessandro
%Y Evert, Stefan
%S Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F attia-etal-2016-cogalex
%X This paper describes our system submission to the CogALex-2016 Shared Task on Corpus-Based Identification of Semantic Relations. Our system won first place for Task-1 and second place for Task-2. The evaluation results of our system on the test set is 88.1% (79.0% for TRUE only) f-measure for Task-1 on detecting semantic similarity, and 76.0% (42.3% when excluding RANDOM) for Task-2 on identifying finer-grained semantic relations. In our experiments, we try word analogy, linear regression, and multi-task Convolutional Neural Networks (CNNs) with word embeddings from publicly available word vectors. We found that linear regression performs better in the binary classification (Task-1), while CNNs have better performance in the multi-class semantic classification (Task-2). We assume that word analogy is more suited for deterministic answers rather than handling the ambiguity of one-to-many and many-to-many relationships. We also show that classifier performance could benefit from balancing the distribution of labels in the training data.
%U https://aclanthology.org/W16-5311/
%P 86-91

Markdown (Informal)

[CogALex-V Shared Task: GHHH - Detecting Semantic Relations via Word Embeddings](https://aclanthology.org/W16-5311/) (Attia et al., CogALex 2016)

• CogALex-V Shared Task: GHHH - Detecting Semantic Relations via Word Embeddings (Attia et al., CogALex 2016)

ACL

• Mohammed Attia, Suraj Maharjan, Younes Samih, Laura Kallmeyer, and Thamar Solorio. 2016. CogALex-V Shared Task: GHHH - Detecting Semantic Relations via Word Embeddings. In Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V), pages 86–91, Osaka, Japan. The COLING 2016 Organizing Committee.