Upgrading event and pattern detection to big data

Cherichi, Soumaya; Faiz, Rim

doi:10.1504/ijcse.2019.10020477

Search citation statements

Order By: Relevance

Paper Sections

Select...

Related Work1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some results show that supervision methods have better classification results (Cherichi and Faiz, 2019). However, the performance of supervised methods is highly dependent on the quality of the designed feature.…”

Section: Related Workmentioning

confidence: 99%

Selection gate-based networks for semantic relation extraction

Sun¹,

Li²,

Shen³

et al. 2021

IJES

View full text Add to dashboard Cite

Semantic relatedness between context information and entities, which is one of the most easily accessible features, has been proven to be very useful for detecting the semantic relation held in the text segment. However, some methods fail to take into account important information between entities and contexts. How to effectively choose the closest and the most relevant information to the entity in context words in a sentence is an important task. In this paper, we propose selection gate-based networks (SGate-NN) to model the relatedness of an entity word with its context words, and select the relevant parts of contexts to infer the semantic relation toward the entity. We conduct experiments using the SemEval-2010 Task 8 dataset. Extensive experiments and the results demonstrate that the proposed method is effective for relation classification, which can obtain state-of-the-art classification accuracy.

show abstract

Section: Related Workmentioning

confidence: 99%