Unsupervised Coreference Resolution Using a Graph Labeling Approach

2014 ◽  
pp. 93-103
Author(s):  
Nafise Sadat Moosavi ◽  
GholamReza GhassemSani
Keyword(s):  
Graph Labeling ◽  
Coreference Resolution ◽  
Labeling Approach

A graph-labeling approach for efficient cone-of-influence computation in model-checking problems with multiple properties

2015 ◽  
Vol 46 (4) ◽  
pp. 493-511 ◽  
Author(s):  
Gianpiero Cabodi ◽  
Paolo Camurati ◽  
Stefano Quer
Keyword(s):  
Model Checking ◽  
Graph Labeling ◽  
Labeling Approach

A Joint Model for Quotation Attribution and Coreference Resolution

2014 ◽  
Author(s):  
Mariana S. C. Almeida ◽  
Miguel B. Almeida ◽  
André F. T. Martins
Keyword(s):  
Joint Model ◽  
Coreference Resolution

A STUDY ON FUZZY EQUITABLE EDGE COLORING OF WHEEL RELATED GRAPHS

2020 ◽  
Vol 9 (11) ◽  
pp. 9311-9317
Author(s):  
K. Sivaraman ◽  
R.V. Prasad
Keyword(s):  
Edge Coloring ◽  
Graph Labeling ◽  
Equitable Edge Coloring

Equitable edge coloring is a kind of graph labeling with the following restrictions. No two adjacent edges receive same label (color). and number of edges in any two color classes differ by at most one. In this work we are going to present the Fuzzy equitable edge coloring of some wheel related graphs.

Multimodal Coreference Resolution for Exploratory Data Visualization Dialogue: Context-Based Annotation and Gesture Identification

2017 ◽  
Author(s):  
Abhinav Kumar ◽  
Jillian Aurisano ◽  
Barbara Di Eugenio ◽  
Andrew Johnson ◽  
Abeer Alsaiari ◽  
...  
Keyword(s):  
Data Visualization ◽  
Coreference Resolution ◽  
Exploratory Data

scholarly journals An optogenetic proximity labeling approach to probe the composition of inducible biomolecular condensates in cultured cells

2021 ◽  
Vol 2 (3) ◽  
pp. 100677
Author(s):  
Emile Alghoul ◽  
Jihane Basbous ◽  
Angelos Constantinou
Keyword(s):  
Cultured Cells ◽  
Labeling Approach

Knowledge Enhanced LSTM for Coreference Resolution on Biomedical Texts

2021 ◽  
Author(s):  
Yufei Li ◽  
Xiaoyong Ma ◽  
Xiangyu Zhou ◽  
Pengzhen Cheng ◽  
Kai He ◽  
...  
Keyword(s):  
Information Integration ◽  
Short Term Memory ◽  
Superior Performance ◽  
Supplementary Information ◽  
Specific Information ◽  
Coreference Resolution ◽  
Fine Grained ◽  
Domain Specific ◽  
Memory Network ◽  
Biomedical Texts

Abstract Motivation Bio-entity Coreference Resolution focuses on identifying the coreferential links in biomedical texts, which is crucial to complete bio-events’ attributes and interconnect events into bio-networks. Previously, as one of the most powerful tools, deep neural network-based general domain systems are applied to the biomedical domain with domain-specific information integration. However, such methods may raise much noise due to its insufficiency of combining context and complex domain-specific information. Results In this paper, we explore how to leverage the external knowledge base in a fine-grained way to better resolve coreference by introducing a knowledge-enhanced Long Short Term Memory network (LSTM), which is more flexible to encode the knowledge information inside the LSTM. Moreover, we further propose a knowledge attention module to extract informative knowledge effectively based on contexts. The experimental results on the BioNLP and CRAFT datasets achieve state-of-the-art performance, with a gain of 7.5 F1 on BioNLP and 10.6 F1 on CRAFT. Additional experiments also demonstrate superior performance on the cross-sentence coreferences. Supplementary information Supplementary data are available at Bioinformatics online.

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