Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text (N18-2)
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| Challenge: | Existing methods for relation classification have been used in natural language processing. |
| Approach: | They propose a relation classification task for Chinese literature text using a new dataset. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on Chinese literature text. |
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| Challenge: | Relation classification is the task to predict semantic relations between pairs of entities in a given text. |
| Approach: | They propose to extract relations between entities in Chinese text using a long-term memory network. |
| Outcome: | The proposed system achieves state-of-the-art F-measure on ACE 2005 corpus . it predicts relations between head entity e h and tail entity t from sentence . |
Neural Relation Classification with Text Descriptions (C18-1)
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| Challenge: | State-of-the-art methods for relation classification suffer from data sparsity issue greatly. |
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Exploratory Neural Relation Classification for Domain Knowledge Acquisition (C18-1)
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| Challenge: | Existing methods for relation classification are limited and lack of low-frequency relations in specific domains. |
| Approach: | They propose a method to learn a classifier on pre-defined relations and discover new relations expressed in texts. |
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Exploiting the Syntax-Model Consistency for Neural Relation Extraction (2020.acl-main)
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| Challenge: | Existing deep learning models for Relation Extraction (RE) have limited generalization beyond the syntactic structures of the input sentences. |
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Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN (N19-1)
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| Challenge: | Recent work on relation classification has gained much success by exploiting deep neural networks. |
| Approach: | They propose a relation classification model using Segment-level Attention-based Convolutional Neural Networks and Dependency-based Recurrent Neural networks. |
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Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks (2020.acl-main)
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| Challenge: | Existing graph-based methods for text classification cannot capture contextual word relationships within each document nor can they produce inductive learning of new words. |
| Approach: | They propose to use Graph Neural Networks to learn the local word representations and then aggregate the word nodes as the document embeddings. |
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Global-to-Local Neural Networks for Document-Level Relation Extraction (2020.emnlp-main)
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| Challenge: | Relation extraction (RE) aims to identify the semantic relations between named entities in text. |
| Approach: | They propose a novel relation extraction model that encodes document information in terms of entity global and local representations and context relation representations. |
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Adaptive Learning of Local Semantic and Global Structure Representations for Text Classification (C18-1)
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Jianyu Zhao, Zhiqiang Zhan, Qichuan Yang, Yang Zhang, Changjian Hu, Zhensheng Li, Liuxin Zhang, Zhiqiang He
| Challenge: | Existing representation models for text classification learn little structure information or rely on pre-defined structures. |
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A Walk-based Model on Entity Graphs for Relation Extraction (P18-2)
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| Challenge: | Existing models treat each relation in a sentence individually, but a graph-based model needs to consider multiple relations between entities to model the dependencies among them. |
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Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities (2020.coling-main)
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| Challenge: | Domain names such as openresearch are being added to a growing set of tokens that an NLP system may need to deal with. |
| Approach: | They propose a tagging model that uses characters as input to break domain names into component words . they propose taagging methods that use concatenated entity names in a large knowledge database . |
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