96 research outputs found
Joint Anaphoricity Detection and Coreference Resolution with Constrained Latent Structures
International audienceThis paper introduces a new structured model for learninganaphoricity detection and coreference resolution in a jointfashion. Specifically, we use a latent tree to represent the fullcoreference and anaphoric structure of a document at a globallevel, and we jointly learn the parameters of the two modelsusing a version of the structured perceptron algorithm.Our joint structured model is further refined by the use ofpairwise constraints which help the model to capture accuratelycertain patterns of coreference. Our experiments on theCoNLL-2012 English datasets show large improvements inboth coreference resolution and anaphoricity detection, comparedto various competing architectures. Our best coreferencesystem obtains a CoNLL score of 81:97 on gold mentions,which is to date the best score reported on this setting
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. The key idea is to directly consider all
spans in a document as potential mentions and learn distributions over possible
antecedents for each. The model computes span embeddings that combine
context-dependent boundary representations with a head-finding attention
mechanism. It is trained to maximize the marginal likelihood of gold antecedent
spans from coreference clusters and is factored to enable aggressive pruning of
potential mentions. Experiments demonstrate state-of-the-art performance, with
a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model
ensemble, despite the fact that this is the first approach to be successfully
trained with no external resources.Comment: Accepted to EMNLP 201
Joint Anaphoricity Detection and Coreference Resolution with Constrained Latent Structures
International audienceThis paper introduces a new structured model for learninganaphoricity detection and coreference resolution in a jointfashion. Specifically, we use a latent tree to represent the fullcoreference and anaphoric structure of a document at a globallevel, and we jointly learn the parameters of the two modelsusing a version of the structured perceptron algorithm.Our joint structured model is further refined by the use ofpairwise constraints which help the model to capture accuratelycertain patterns of coreference. Our experiments on theCoNLL-2012 English datasets show large improvements inboth coreference resolution and anaphoricity detection, comparedto various competing architectures. Our best coreferencesystem obtains a CoNLL score of 81:97 on gold mentions,which is to date the best score reported on this setting
Review of coreference resolution in English and Persian
Coreference resolution (CR) is one of the most challenging areas of natural
language processing. This task seeks to identify all textual references to the
same real-world entity. Research in this field is divided into coreference
resolution and anaphora resolution. Due to its application in textual
comprehension and its utility in other tasks such as information extraction
systems, document summarization, and machine translation, this field has
attracted considerable interest. Consequently, it has a significant effect on
the quality of these systems. This article reviews the existing corpora and
evaluation metrics in this field. Then, an overview of the coreference
algorithms, from rule-based methods to the latest deep learning techniques, is
provided. Finally, coreference resolution and pronoun resolution systems in
Persian are investigated.Comment: 44 pages, 11 figures, 5 table
Resolució de la correferència usant aprenentatge discriminatiu amb estructures latents
Projecte orientat a la recerca en el qual s'implementa un sistema d'anà lisi automà tica de la coreferència amb tècniques d'aprenentatge automà tic supervisat (a partir d'un corpus d'exemples)
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