3 research outputs found
Wolves at SemEval-2018 task 10: Semantic discrimination based on knowledge and association
This paper describes the system submitted to
SemEval 2018 shared task 10 ‘Capturing Discriminative
Attributes’. We use a combination
of knowledge-based and co-occurrence
features to capture the semantic difference between
two words in relation to an attribute. We
define scores based on association measures,
ngram counts, word similarity, and ConceptNet
relations. The system is ranked 4th (joint)
on the official leaderboard of the task.Research Group in Computational Linguistic
Wolves at SemEval-2018 Task 10: Semantic Discrimination based on Knowledge and Association
This paper describes the system submitted to
SemEval 2018 shared task 10 ���Capturing Discriminative
Attributes���. We use a combination
of knowledge-based and co-occurrence
features to capture the semantic difference between
two words in relation to an attribute. We
define scores based on association measures,
ngram counts, word similarity, and ConceptNet
relations. The system is ranked 4th (joint)
on the official leaderboard of the task.Research Group in Computational Linguistic
Detecting semantic difference: a new model based on knowledge and collocational association
This is an accepted manuscript of an article published by John Benjamins Publishing Company in Computational Phraseology edited by G Corpas Pastor & J-P Colson on 08/05/2020, available online: https://doi.org/10.1075/ivitra.24.16tas
The accepted version of the publication may differ from the final published version.Semantic discrimination among concepts is a daily exercise for humans when using natural languages. For example, given the words, airplane and car, the word flying can easily be thought and used as an attribute to differentiate them. In this study, we propose a novel automatic approach to detect whether an attribute word represents the difference between two given words. We exploit a combination of knowledge-based and co-occurrence features (collocations) to capture the semantic difference between two words in relation to an attribute. The features are scores that are defined for each pair of words and an attribute, based on association measures, n-gram counts, word similarity, and Concept-Net relations. Based on these features we designed a system that run several experiments on a SemEval-2018 dataset. The experimental results indicate that the proposed model performs better, or at least comparable with, other systems evaluated on the same data for this task.Published versio