Relation extraction has been widely used for finding unknown relational facts from the plain text. Most existing methods focus on exploiting mono-lingual data for relation extraction, ignoring massive information from the texts in various languages. To address this issue, we introduce a multi-lingual neural relation extraction framework, which employs monolingual attention to utilize the inform...

Distant supervised relation extraction has been widely used to find novel relational facts from text. However, distant supervision inevitably accompanies with the wrong labelling problem, and these noisy data will substantially hurt the performance of relation extraction. To alleviate this issue, we propose a sentence-level attention-based model for relation extraction. In this model, we employ...

Relation extraction is one of the core challenges in automated knowledge base construction. One line of approach for relation extraction is to perform multi-hop reasoning on the paths connecting an entity pair to infer new relations. While these methods have been successfully applied for knowledge base completion, they do not utilize the entity or the entity type information to make predictions...

Automatic extraction of semantic relationships between entity instances in an ontology is useful for attaching richer semantic metadata to documents. In this paper we propose an SVM based approach to hierarchical relation extraction, using features derived automatically from a number of GATE-based open-source language processing tools. In comparison to the previous works, we use several new fea...

The two most important tasks in entity information summarization from the Web are named entity recognition and relation extraction. Little work has been done toward an integrated statistical model for understanding both named entities and their relationships. Most of the previous works on relation extraction assume the named entities are pre-given. The drawbacks of these sequential models are t...

Building accurate knowledge graphs is essential for question answering system. We suggest a crowd-to-machine relation extraction system to eventually fill a knowledge graph. To train a relation extraction model, training data first have to be prepared either manually or automatically. A model trained by manually labeled data could show a better performance, however, it is not scalable because a...

Even though there are many databases for gene/protein interactions, most such data still exist only in the biomedical literature. They are spread in biomedical literature written in natural languages and they require much effort such as data mining for constructing well-structured data forms. As genomic research advances, knowledge discovery from a large collection of scientific papers is becom...

Extracting the relations that exist between two entities is an important step in numerous Web-related tasks such as information extraction. A supervised relation extraction system that is trained to extract a particular relation type might not accurately extract a new type of a relation for which it has not been trained. However, it is costly to create training data manually for every new relat...

Organized relational knowledge in the form of “knowledge graphs” is important for many applications. However, the ability to populate knowledge bases with facts automatically extracted from documents has improved frustratingly slowly. This paper simultaneously addresses two issues that have held back prior work. We first propose an effective new model, which combines an LSTM sequence model with...

Relation extraction from texts is a research topic since the message understanding conferences. Most investigations dealt with English texts. However, the heuristics found for these do not perform well when applied to a language with free word order, as is, e.g., German. In this paper, we present a German annotated corpus for relation extraction. We have implemented the state of the art methods...