Eugene Kostrov
Niall Ferguson is prolific, well-paid and a snappy dresser. Stephen Moss hates him.
Niall Ferguson is prolific, well-paid and a snappy dresser. Stephen Moss hates him.
Niall Ferguson is prolific, well-paid and a snappy dresser. Stephen Moss hates him.
Designed as an improvement to Turing test
Designed as an improvement to Turing test
The trophy would not fit in the brown suitcase because it was too big. What was too big?
Designed as an improvement to Turing test
The trophy would not fit in the brown suitcase because it was too big. What was too big?
The trophy would not fit in the brown suitcase because it was too small. What was too small?
1. Begin at the NP node immediately dominating the pronoun
2. Go up the tree to the first NP or S node encountered. Call this node X and the path used to reach it p.
3. Traverse all branches below node X to the left of path p in a left-to-right, breadth-first fashion. Propose as an antecedent any NP node that is encountered which has an NP or S node between it and X.
4. If node X is the highest S node in the sentence, traverse the surface parse trees of previous sentences in the text in order of recency, the most recent first; each tree is traversed in a left-to-right, breadth-first manner, and when an NP node is encountered, it is proposed as an antecedent. If X is not the highest S node in the sentence, continue to step 5.
...
5. From node X, go up the tree to the first NP or S node encountered. Call this new node X, and call the path traversed to reach it p.
6. If X is an NP node and if the path p to X did not pass through the Nominal node that X immediately dominates, propose X as the antecedent.
7. Traverse all branches below node X to the left of path p in a left-to-right, breadth-first manner. Propose any NP node encountered as the antecedent.
8. If X is an S node, traverse all the branches of node X to the right of path p in a left-to-right, breadth-first manner, but do not go below any NP or S node encountered. Propose any NP node encountered as the antecedent.
9. Go to step 4.
GAP Coreference Dataset - https://github.com/google-research-datasets/gap-coreference
Average tokens in text = ~71.5
Average sentences in text = ~2
| Precision | Recall | F1 | |
|---|---|---|---|
| Hobbs | 0.153 | 0.156 | 0.155 |
| Hobbs CNF | 0.087 | 0.088 | 0.087 |
| AllenNLP | 0.340 | 0.386 | 0.356 |
Pros:
Cons: