@inproceedings{shiue-etal-2018-ntu,
title = "{NTU} {NLP} Lab System at {S}em{E}val-2018 Task 10: Verifying Semantic Differences by Integrating Distributional Information and Expert Knowledge",
author = "Shiue, Yow-Ting and
Huang, Hen-Hsen and
Chen, Hsin-Hsi",
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/S18-1171",
doi = "10.18653/v1/S18-1171",
pages = "1027--1033",
abstract = "This paper presents the NTU NLP Lab system for the SemEval-2018 Capturing Discriminative Attributes task. Word embeddings, pointwise mutual information (PMI), ConceptNet edges and shortest path lengths are utilized as input features to build binary classifiers to tell whether an attribute is discriminative for a pair of concepts. Our neural network model reaches about 73{\%} F1 score on the test set and ranks the 3rd in the task. Though the attributes to deal with in this task are all visual, our models are not provided with any image data. The results indicate that visual information can be derived from textual data.",
}
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<abstract>This paper presents the NTU NLP Lab system for the SemEval-2018 Capturing Discriminative Attributes task. Word embeddings, pointwise mutual information (PMI), ConceptNet edges and shortest path lengths are utilized as input features to build binary classifiers to tell whether an attribute is discriminative for a pair of concepts. Our neural network model reaches about 73% F1 score on the test set and ranks the 3rd in the task. Though the attributes to deal with in this task are all visual, our models are not provided with any image data. The results indicate that visual information can be derived from textual data.</abstract>
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%0 Conference Proceedings
%T NTU NLP Lab System at SemEval-2018 Task 10: Verifying Semantic Differences by Integrating Distributional Information and Expert Knowledge
%A Shiue, Yow-Ting
%A Huang, Hen-Hsen
%A Chen, Hsin-Hsi
%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 shiue-etal-2018-ntu
%X This paper presents the NTU NLP Lab system for the SemEval-2018 Capturing Discriminative Attributes task. Word embeddings, pointwise mutual information (PMI), ConceptNet edges and shortest path lengths are utilized as input features to build binary classifiers to tell whether an attribute is discriminative for a pair of concepts. Our neural network model reaches about 73% F1 score on the test set and ranks the 3rd in the task. Though the attributes to deal with in this task are all visual, our models are not provided with any image data. The results indicate that visual information can be derived from textual data.
%R 10.18653/v1/S18-1171
%U https://aclanthology.org/S18-1171
%U https://doi.org/10.18653/v1/S18-1171
%P 1027-1033
Markdown (Informal)
[NTU NLP Lab System at SemEval-2018 Task 10: Verifying Semantic Differences by Integrating Distributional Information and Expert Knowledge](https://aclanthology.org/S18-1171) (Shiue et al., SemEval 2018)
ACL