@inproceedings{vinayan-etal-2018-amritanlp,
title = "{A}mrita{NLP} at {S}em{E}val-2018 Task 10: Capturing discriminative attributes using convolution neural network over global vector representation.",
author = "Vinayan, Vivek and
Kumar M, Anand and
K P, Soman",
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1166",
doi = "10.18653/v1/S18-1166",
pages = "1003--1007",
abstract = "The {``}Capturing Discriminative Attributes{''} sharedtask is the tenth task, conjoint with SemEval2018. The task is to predict if a word can capture distinguishing attributes of one word from another. We use GloVe word embedding, pre-trained on openly sourced corpus for this task. A base representation is initially established over varied dimensions. These representations are evaluated based on validation scores over two models, first on an SVM based classifier and second on a one dimension CNN model. The scores are used to further develop the representation with vector combinations, by considering various distance measures. These measures correspond to offset vectors which are concatenated as features, mainly to improve upon the F1score, with the best accuracy. The features are then further tuned on the validation scores, to achieve highest F1score. Our evaluation narrowed down to two representations, classified on CNN models, having a total dimension length of 1204 {\&} 1203 for the final submissions. Of the two, the latter feature representation delivered our best F1score of 0.658024 (as per result).",
}
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%0 Conference Proceedings
%T AmritaNLP at SemEval-2018 Task 10: Capturing discriminative attributes using convolution neural network over global vector representation.
%A Vinayan, Vivek
%A Kumar M, Anand
%A K P, Soman
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F vinayan-etal-2018-amritanlp
%X The “Capturing Discriminative Attributes” sharedtask is the tenth task, conjoint with SemEval2018. The task is to predict if a word can capture distinguishing attributes of one word from another. We use GloVe word embedding, pre-trained on openly sourced corpus for this task. A base representation is initially established over varied dimensions. These representations are evaluated based on validation scores over two models, first on an SVM based classifier and second on a one dimension CNN model. The scores are used to further develop the representation with vector combinations, by considering various distance measures. These measures correspond to offset vectors which are concatenated as features, mainly to improve upon the F1score, with the best accuracy. The features are then further tuned on the validation scores, to achieve highest F1score. Our evaluation narrowed down to two representations, classified on CNN models, having a total dimension length of 1204 & 1203 for the final submissions. Of the two, the latter feature representation delivered our best F1score of 0.658024 (as per result).
%R 10.18653/v1/S18-1166
%U https://aclanthology.org/S18-1166
%U https://doi.org/10.18653/v1/S18-1166
%P 1003-1007
Markdown (Informal)
[AmritaNLP at SemEval-2018 Task 10: Capturing discriminative attributes using convolution neural network over global vector representation.](https://aclanthology.org/S18-1166) (Vinayan et al., SemEval 2018)
ACL