A review of relational machine learning for knowledge graphs
Relational machine learning studies methods for the statistical analysis of relational, or
graph-structured, data. In this paper, we provide a review of how such statistical models can …
graph-structured, data. In this paper, we provide a review of how such statistical models can …
Cross-Sentence N-ary Relation Extraction with Graph LSTMs
Past work in relation extraction has focused on binary relations in single sentences. Recent
NLP inroads in high-value domains have sparked interest in the more general setting of …
NLP inroads in high-value domains have sparked interest in the more general setting of …
Natural language processing for information extraction
S Singh - arxiv preprint arxiv:1807.02383, 2018 - arxiv.org
With rise of digital age, there is an explosion of information in the form of news, articles,
social media, and so on. Much of this data lies in unstructured form and manually managing …
social media, and so on. Much of this data lies in unstructured form and manually managing …
Graph long short term memory for syntactic relationship discovery
CB Quirk, KN Toutanova, W Yih, H Poon… - US Patent …, 2019 - Google Patents
Long short term memory units that accept a non-predefined number of inputs are used to
provide natural language relation extraction over a user-specified range on content. Content …
provide natural language relation extraction over a user-specified range on content. Content …
Highlife: Higher-arity fact harvesting
Text-based knowledge extraction methods for populating knowledge bases have focused on
binary facts: relationships between two entities. However, in advanced domains such as …
binary facts: relationships between two entities. However, in advanced domains such as …
Sar-graphs: A language resource connecting linguistic knowledge with semantic relations from knowledge graphs
Recent years have seen a significant growth and increased usage of large-scale knowledge
resources in both academic research and industry. We can distinguish two main types of …
resources in both academic research and industry. We can distinguish two main types of …
Linked Lexical Knowledge Bases
Linked Lexical Knowledge Bases Page 1 21 CHAPTER 2 Linked Lexical Knowledge Bases In
this chapter, we move closer to the core of this book: the linking of LKBs. To this end, we first …
this chapter, we move closer to the core of this book: the linking of LKBs. To this end, we first …
Cross-sentence n-ary relation extraction using lower-arity universal schemas
K Akimoto, T Hiraoka, K Sadamasa… - Proceedings of the 2019 …, 2019 - aclanthology.org
Most existing relation extraction approaches exclusively target binary relations, and n-ary
relation extraction is relatively unexplored. Current state-of-the-art n-ary relation extraction …
relation extraction is relatively unexplored. Current state-of-the-art n-ary relation extraction …
A Reinforcement Learning Framework for N-Ary Document-Level Relation Extraction
Knowledge Bases (KBs) have become more complex because some facts in KBs include
more than two entities. The construction and completion of these KBs require a new relation …
more than two entities. The construction and completion of these KBs require a new relation …
Stuffie: Semantic tagging of unlabeled facets using fine-grained information extraction
Recent knowledge extraction methods are moving towards ternary and higher-arity relations
to capture more information about binary facts. An example is to include the time, the …
to capture more information about binary facts. An example is to include the time, the …