Research map / Label-efficient and robust learning / Recognition and long-tail
Understanding Enthymemes in Deductive Argumentation Using Semantic Distance Measures
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GloVe cosine distances to distinguish words in the same WordNet synset from words in different synsets | GloVe 6B.300d · WordNet artefact | cosine similarity distance: 0.79 with standard deviation of 0.19; 0.97 with standard deviation of 0.10 | Mixed |
| Increase the number of swaps in the substitution | SUB inference procedure · Randomly generated knowledgebases in Figure 1 | inference frequency | Helped |
| Increase the ratio of incoming symbols to all atoms | SUB inference procedure · Randomly generated knowledgebases in Figure 1 | inference frequency | Helped |
| Allow approximate rather than necessarily minimal abductive solutions | ABDUCE · Knowledgebase of 1000 clauses, 1000 defaults, 100 swaps, 100 atoms | runtime: 0.24 secs | Helped |
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