Many applications in information extraction, natural language understanding, information retrieval require an understanding of the semantic relations between entities. We present a comprehensive review of various aspects of the entity relation extraction task. We also cover relation extraction task in the medical domain and various challenges and useful resources available in the medical domain.

This paper describes a novel approach to the semantic relation detection problem. Instead of relying only on the training instances for a new relation, we leverage the knowledge learned from previously trained relation detectors. Specifically, we detect a new semantic relation by projecting the new relation’s training instances onto a lower dimension topic space constructed from existing relati...

Distantly supervised relation extraction has been widely used to find novel relational facts from plain text. To predict the relation between a pair of two target entities, existing methods solely rely on those direct sentences containing both entities. In fact, there are also many sentences containing only one of the target entities, which also provide rich useful information but not yet emplo...

Recent studies have shown that embedding textual relations using deep neural networks greatly helps relation extraction. However, many existing studies rely on supervised learning; their performance is dramatically limited by the availability of training data. In this work, we generalize textual relation embedding to the distant supervision setting, where much largerscale but noisy training dat...

We experiment with neural architectures for temporal relation extraction and establish a new state-of-the-art for several scenarios. We find that neural models with only tokens as input outperform state-ofthe-art hand-engineered feature-based models, that convolutional neural networks outperform LSTM models, and that encoding relation arguments with XML tags outperforms a traditional position-b...

Artificial Intelligent systems can benefit from incorporating commonsense knowledge as background, such as ice is cold (HASPROPERTY), chewing is a sub-event of eating (HASSUBEVENT), chair and table are typically found near each other (LOCATEDNEAR), etc. This kind of commonsense facts have been utilized in many downstream tasks, such as textual entailment [4, 1] and visual recognition tasks [29]...

With the advent of the Internet, large amount of digital text is generated everyday in the form of news articles, research publications, blogs, question answering forums and social media. It is important to develop techniques for extracting information automatically from these documents, as lot of important information is hidden within them. This extracted information can be used to improve acc...

A vast amount of usable electronic data is in the form of unstructured text. The relation extraction task aims to identify useful information in text (e.g., PersonW works for OrganisationX, GeneY encodes ProteinZ) and recode it in a format such as a relational database that can be more effectively used for querying and automated reasoning. However, adapting conventional relation extraction syst...

Although much work on relation extraction has aimed at obtaining static facts, many of the target relations are actually fluents, as their validity is naturally anchored to a certain time period. This paper proposes a methodological approach to temporally anchored relation extraction. Our proposal performs distant supervised learning to extract a set of relations from a natural language corpus,...

This paper presents a supervised method for the detection and extraction of Causal Relations from open domain text. First we give a brief outline of the definition of causation and how it relates to other Semantic Relations, as well as a characterization of their encoding. In this work, we only consider marked and explicit causations. Our approach first identifies the syntactic patterns that ma...