Simultaneously Linking Entities and Extracting Relations from Biomedical Text without Mention-Level Supervision

Extracting Higher Order Relations From Biomedical Text

Argumentation in a scientific article is composed of unexpressed and explicit statements of old and new knowledge combined into a logically coherent textual argument. Discourse relations, linguistic coherence relations that connect discourse segments, help to communicate an argument’s logical steps. A biomedical relation exhibits a relationship between biomedical entities. In this paper, we are...

Extracting microRNA-gene relations from biomedical literature using distant supervision

Many biomedical relation extraction approaches are based on supervised machine learning, requiring an annotated corpus. Distant supervision aims at training a classifier by combining a knowledge base with a corpus, reducing the amount of manual effort necessary. This is particularly useful for biomedicine because many databases and ontologies have been made available for many biological process...

Extracting Spatial Entities and Relations in Korean Text

A spatial information extraction system retrieves spatial entities and their relationships for geological searches and reasoning. Spatial information systems have been developed mainly for English text, e.g., through the SpaceEval competition. Some of the techniques are useful but not directly applicable to Korean text, because of linguistic differences and the lack of language resources. In th...

Extracting Relations from Unstructured Text

Extracting Molecular Binding Relationships from Biomedical Text

ARBITER is a Prolog program that extracts assertions about macromolecular binding relationships from biomedical text. We describe the domain knowledge and the underspecified linguistic analyses that support the identification of these predications. After discussing a formal evaluation of ARBITER, we report on its application to 491,000 MEDLINE ~ abstracts, during which almost 25,000 binding rel...