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Coreference Resolution

29 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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Greatest papers with code

Deep contextualized word representations

HLT 2018 zalandoresearch/flair

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). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus.

COREFERENCE RESOLUTION LANGUAGE MODELLING NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC ROLE LABELING SENTIMENT ANALYSIS

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

HLT 2018 kentonl/e2e-coref

We introduce a fully differentiable approximation to higher-order inference for coreference resolution. Our approach uses the antecedent distribution from a span-ranking architecture as an attention mechanism to iteratively refine span representations.

COREFERENCE RESOLUTION

End-to-end Neural Coreference Resolution

EMNLP 2017 kentonl/e2e-coref

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. The key idea is to directly consider all spans in a document as potential mentions and learn distributions over possible antecedents for each.

COREFERENCE RESOLUTION

Deep Reinforcement Learning for Mention-Ranking Coreference Models

EMNLP 2016 clarkkev/deep-coref

Coreference resolution systems are typically trained with heuristic loss functions that require careful tuning. In this paper we instead apply reinforcement learning to directly optimize a neural mention-ranking model for coreference evaluation metrics.

COREFERENCE RESOLUTION

Improving Coreference Resolution by Learning Entity-Level Distributed Representations

ACL 2016 clarkkev/deep-coref

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. We present a neural network based coreference system that produces high-dimensional vector representations for pairs of coreference clusters.

COREFERENCE RESOLUTION

Dynamic Entity Representations in Neural Language Models

EMNLP 2017 smartschat/cort

Understanding a long document requires tracking how entities are introduced and evolve over time. We present a new type of language model, EntityNLM, that can explicitly model entities, dynamically update their representations, and contextually generate their mentions.

COREFERENCE RESOLUTION LANGUAGE MODELLING

Learning Global Features for Coreference Resolution

HLT 2016 swiseman/nn_coref

There is compelling evidence that coreference prediction would benefit from modeling global information about entity-clusters. Yet, state-of-the-art performance can be achieved with systems treating each mention prediction independently, which we attribute to the inherent difficulty of crafting informative cluster-level features.

COREFERENCE RESOLUTION

Syntactic Scaffolds for Semantic Structures

EMNLP 2018 swabhs/scaffolding

We introduce the syntactic scaffold, an approach to incorporating syntactic information into semantic tasks. Syntactic scaffolds avoid expensive syntactic processing at runtime, only making use of a treebank during training, through a multitask objective.

COREFERENCE RESOLUTION