UWB at SemEval-2018 Task 10: Capturing Discriminative Attributes from Word Distributions
We present our UWB system for the task of capturing discriminative attributes at SemEval 2018. Given two words and an attribute, the system decides, whether this attribute is discriminative between the words or not. Assuming Distributional Hypothesis, i.e., a word meaning is related to the distribution across contexts, we introduce several approaches to compare word contextual information. We experiment with state-of-the-art semantic spaces and with simple co-occurrence statistics. We show the word distribution in the corpus has potential for detecting discriminative attributes. Our system achieves F1 score 72.1{\%} and is ranked {\#}4 among 26 submitted systems.
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Datasets
ConceptNet
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Relation Extraction | SemEval 2018 Task 10 | LexVec, word co-occurrence, and ConceptNet data combined using maximum entropy classifier | F1-Score | 0.72 | # 4 |
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