Browse > Natural Language Processing > Coreference Resolution

# Coreference Resolution Edit

36 papers with code · Natural Language Processing

Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities.

Example:

               +-----------+
|           |
I voted for Obama because he was most aligned with my values", she said.
|                                                 |            |
+-------------------------------------------------+------------+


"I", "my", and "she" belong to the same cluster and "Obama" and "he" belong to the same cluster.

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# Deep contextualized word representations

We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).

6,335

# Higher-order Coreference Resolution with Coarse-to-fine Inference

We introduce a fully differentiable approximation to higher-order inference for coreference resolution.

271

# End-to-end Neural Coreference Resolution

We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered mention detector.

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# Deep Reinforcement Learning for Mention-Ranking Coreference Models

Coreference resolution systems are typically trained with heuristic loss functions that require careful tuning.

192

# Improving Coreference Resolution by Learning Entity-Level Distributed Representations

A long-standing challenge in coreference resolution has been the incorporation of entity-level information - features defined over clusters of mentions instead of mention pairs.

192

# Dynamic Entity Representations in Neural Language Models

Understanding a long document requires tracking how entities are introduced and evolve over time.

112

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# Learning Global Features for Coreference Resolution

There is compelling evidence that coreference prediction would benefit from modeling global information about entity-clusters.

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