Detecting semantic difference
A new model based on knowledge and collocational
association
Shiva Taslimipoor | Research Group in Computational Linguistics, University of
Wolverhampton
Omid Rohanian | Research Group in Computational Linguistics, University of
Wolverhampton
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.
Article outline
- 1.Introduction
- 2.Related work
- 3.Methodology
- 3.1Association-based score
- 3.2Google N-Grams
- 3.3Word embedding-based score
- 3.4ConceptNet score
- 4.Experiments
- 4.1Data
- 4.2Experimental setup
- 4.3Evaluation metrics
- 5.Results and discussion
- 6.Conclusion
-
Notes
-
References