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A comprehensive survey on automatic knowledge graph construction
Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …
knowledge. To this end, much effort has historically been spent extracting informative fact …
More data, more relations, more context and more openness: A review and outlook for relation extraction
Relational facts are an important component of human knowledge, which are hidden in vast
amounts of text. In order to extract these facts from text, people have been working on …
amounts of text. In order to extract these facts from text, people have been working on …
COMET: Commonsense transformers for automatic knowledge graph construction
We present the first comprehensive study on automatic knowledge base construction for two
prevalent commonsense knowledge graphs: ATOMIC (Sap et al., 2019) and ConceptNet …
prevalent commonsense knowledge graphs: ATOMIC (Sap et al., 2019) and ConceptNet …
Effective modeling of encoder-decoder architecture for joint entity and relation extraction
A relation tuple consists of two entities and the relation between them, and often such tuples
are found in unstructured text. There may be multiple relation tuples present in a text and …
are found in unstructured text. There may be multiple relation tuples present in a text and …
[HTML][HTML] Open-cykg: An open cyber threat intelligence knowledge graph
Instant analysis of cybersecurity reports is a fundamental challenge for security experts as
an immeasurable amount of cyber information is generated on a daily basis, which …
an immeasurable amount of cyber information is generated on a daily basis, which …
Neural relation extraction for knowledge base enrichment
We study relation extraction for knowledge base (KB) enrichment. Specifically, we aim to
extract entities and their relationships from sentences in the form of triples and map the …
extract entities and their relationships from sentences in the form of triples and map the …
Openie6: Iterative grid labeling and coordination analysis for open information extraction
A recent state-of-the-art neural open information extraction (OpenIE) system generates
extractions iteratively, requiring repeated encoding of partial outputs. This comes at a …
extractions iteratively, requiring repeated encoding of partial outputs. This comes at a …
Continual relation learning via episodic memory activation and reconsolidation
Continual relation learning aims to continually train a model on new data to learn
incessantly emerging novel relations while avoiding catastrophically forgetting old relations …
incessantly emerging novel relations while avoiding catastrophically forgetting old relations …
AGRONER: An unsupervised agriculture named entity recognition using weighted distributional semantic model
In this work, we propose a novel weighted distributional semantic model for unsupervised
Named Entity Recognition (NER) in domain specific texts, specifically focusing on …
Named Entity Recognition (NER) in domain specific texts, specifically focusing on …
A survey on neural open information extraction: Current status and future directions
Open Information Extraction (OpenIE) facilitates domain-independent discovery of relational
facts from large corpora. The technique well suits many open-world natural language …
facts from large corpora. The technique well suits many open-world natural language …