scholarly journals Research on syntactic dependency tree and Ontology constraint in remote Supervising relation extraction

2021 ◽  
Vol 1748 ◽  
pp. 032051
Author(s):  
Shuxu Zhao ◽  
Yiguang Zhang ◽  
Xiaolong Wang
Keyword(s):  
Relation Extraction ◽  
Dependency Tree ◽  
Syntactic Dependency

A computational model for measuring discourse complexity

2019 ◽  
Vol 21 (6) ◽  
pp. 690-712 ◽  
Author(s):  
Kun Sun ◽  
Wenxin Xiong
Keyword(s):  
Computational Model ◽  
Discourse Structure ◽  
Structure Theory ◽  
Dependency Tree ◽  
Discourse Relations ◽  
New Perspective ◽  
Discourse Units ◽  
Syntactic Dependency ◽  
Regular Patterns ◽  
Quantitative Approaches

In past studies, the few quantitative approaches to discourse structure were mostly confined to the presentation of the frequency of discourse relations. However, quantitative approaches should take into account both hierarchical and relational layers in the discourse structure. This study considers these factors and addresses the issue of how discourse relations and discourse units are related. It draws upon the available corpora of discourse structure (rhetorical structure theory-discourse treebank (RST-DT)) from a new perspective. Since an RST tree can be converted into a syntactic dependency tree, the data extracted from the RST-DT can be useful for calculating the discourse distance in much the same way as syntactic dependency distance is calculated. Discourse distance is also applicable to measuring the depth of the human processing of discourse. Furthermore, the data derived from the RST-DT are also easily converted into network data. This study finds that discourse structure has its discourse distance minimum and each type of RST relations has its range of discourse distance. The frequency distribution of discourse data basically follows the power law on several levels, while a network approach reveals how discourse units are arranged spatially in regular patterns. The two methods are mutually complementary in revealing the interaction between discourse relations and discourse units in a comprehensive manner, as well as in revealing how people process and comprehend discourse dynamically. Accordingly, we propose merging the two methods so as to yield a computational model for assessing discourse complexity and comprehension.

scholarly journals Software Knowledge Entity Relation Extraction with Entity-Aware and Syntactic Dependency Structure Information

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Mingjing Tang ◽  
Tong Li ◽  
Wei Wang ◽  
Rui Zhu ◽  
Zifei Ma ◽  
...  
Keyword(s):  
Large Scale ◽  
Complex Structure ◽  
Relation Extraction ◽  
Knowledge Graph ◽  
Knowledge Domain ◽  
Convolutional Network ◽  
Knowledge Community ◽  
Structure Information ◽  
Entity Relation Extraction ◽  
Syntactic Dependency

Software knowledge community contains a large scale of software knowledge entities with complex structure and rich semantic relations. Semantic relation extraction of software knowledge entities is a critical task for software knowledge graph construction, which has an important impact on knowledge graph based tasks such as software document generation and software expert recommendation. Due to the problems of entity sparsity, relation ambiguity, and the lack of annotated dataset in user-generated content of software knowledge community, it is difficult to apply existing methods of relation extraction in the software knowledge domain. To address these issues, we propose a novel software knowledge entity relation extraction model which incorporates entity-aware information with syntactic dependency information. Bidirectional Gated Recurrent Unit (Bi-GRU) and Graph Convolutional Networks (GCN) are used to learn the features of contextual semantic representation and syntactic dependency representation, respectively. To obtain more syntactic dependency information, a weight graph convolutional network based on Newton’s cooling law is constructed by calculating a weight adjacency matrix. Specifically, an entity-aware attention mechanism is proposed to integrate the entity information and syntactic dependency information to improve the prediction performance of the model. Experiments are conducted on a dataset which is constructed based on texts of the StackOverflow and show that the proposed model has better performance than the benchmark models.

scholarly journals Relation Extraction with Convolutional Network over Learnable Syntax-Transport Graph

2020 ◽  
Vol 34 (05) ◽  
pp. 8928-8935
Author(s):  
Kai Sun ◽  
Richong Zhang ◽  
Yongyi Mao ◽  
Samuel Mensah ◽  
Xudong Liu
Keyword(s):  
Relation Extraction ◽  
Weighted Graph ◽  
Relevant Information ◽  
Irrelevant Information ◽  
Classification Task ◽  
Convolutional Network ◽  
Structure Information ◽  
Convolutional Networks ◽  
Dependency Tree ◽  
Relation Classification

A large majority of approaches have been proposed to leverage the dependency tree in the relation classification task. Recent works have focused on pruning irrelevant information from the dependency tree. The state-of-the-art Attention Guided Graph Convolutional Networks (AGGCNs) transforms the dependency tree into a weighted-graph to distinguish the relevance of nodes and edges for relation classification. However, in their approach, the graph is fully connected, which destroys the structure information of the original dependency tree. How to effectively make use of relevant information while ignoring irrelevant information from the dependency trees remains a challenge in the relation classification task. In this work, we learn to transform the dependency tree into a weighted graph by considering the syntax dependencies of the connected nodes and persisting the structure of the original dependency tree. We refer to this graph as a syntax-transport graph. We further propose a learnable syntax-transport attention graph convolutional network (LST-AGCN) which operates on the syntax-transport graph directly to distill the final representation which is sufficient for classification. Experiments on Semeval-2010 Task 8 and Tacred show our approach outperforms previous methods.

scholarly journals Learning Latent Forests for Medical Relation Extraction

2020 ◽  
Author(s):  
Zhijiang Guo ◽  
Guoshun Nan ◽  
Wei LU ◽  
Shay B. Cohen
Keyword(s):  
Error Propagation ◽  
Latent Variable ◽  
State Of The Art ◽  
Relation Extraction ◽  
Medical Texts ◽  
Tree Structures ◽  
Dependency Structure ◽  
Dependency Tree ◽  
Non Local ◽  
Novel Model

The goal of medical relation extraction is to detect relations among entities, such as genes, mutations and drugs in medical texts. Dependency tree structures have been proven useful for this task. Existing approaches to such relation extraction leverage off-the-shelf dependency parsers to obtain a syntactic tree or forest for the text. However, for the medical domain, low parsing accuracy may lead to error propagation downstream the relation extraction pipeline. In this work, we propose a novel model which treats the dependency structure as a latent variable and induces it from the unstructured text in an end-to-end fashion. Our model can be understood as composing task-specific dependency forests that capture non-local interactions for better relation extraction. Extensive results on four datasets show that our model is able to significantly outperform state-of-the-art systems without relying on any direct tree supervision or pre-training.

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