AmritaNLP at SemEval-2018 Task 10: Capturing discriminative attributes using convolution neural network over global vector representation.

Vivek Vinayan, Anand Kumar M, Soman K P


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).
Anthology ID:
S18-1166
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
1003–1007
Language:
URL:
https://www.aclweb.org/anthology/S18-1166
DOI:
10.18653/v1/S18-1166
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1166.pdf