Biomedical relation extraction aims to uncover high-quality relations from life science literature with high accuracy and efficiency. Early biomedical relation extraction tasks focused on capturing binary relations, such as protein-protein interactions, which are crucial for virtually every process in a living cell. Information about these interactions provides the foundations for new therapeut...

This paper presents a new method for extracting meaningful relations from unstructured natural language sources. The method is based on information made available by shallow semantic parsers. Semantic information was used (1) to enhance a dependency tree kernel; and (2) to build semantic dependency structures used for enhanced relation extraction for several semantic classifiers. In our experim...

We present a new kernel method for extracting semantic relations between entities in natural language text, based on a generalization of subsequence kernels. This kernel uses three types of subsequence patterns that are typically employed in natural language to assert relationships between two entities. Experiments on extracting protein interactions from biomedical corpora and top-level relatio...

The automatic extraction of relations between entities expressed in natural language text is an important problem for IR and text understanding. In this paper we show how different kernels for parse trees can be combined to improve the relation extraction quality. On a public benchmark dataset the combination of a kernel for phrase grammar parse trees and for dependency parse trees outperforms ...

Relation extraction methods are essential for creating robust text mining tools to help researchers find useful knowledge in the vast published literature. Easy-touse and generalizable methods are needed to encourage an ecosystem in which researchers can easily use shared resources and build upon each others’ methods. We present the Kindred Python package1 for relation extraction. It builds upo...

Relation extraction is an important step in Ontology construction. This paper provides a method to extract relations among conceptions with AGROVOC.

Previous open Relation Extraction (open RE) approaches mainly rely on linguistic patterns and constraints to extract important relational triples from large-scale corpora. However, they lack of abilities to cover diverse relation expressions or measure the relative importance of candidate triples within a sentence. It is also challenging to name the relation type of a relational triple merely b...

Adversarial training is a mean of regularizing classification algorithms by generating adversarial noise to the training data. We apply adversarial training in relation extraction within the multi-instance multi-label learning framework. We evaluate various neural network architectures on two different datasets. Experimental results demonstrate that adversarial training is generally effective f...

This proposal is for a programme of work leading to the building of a data querying application. The starting point is a collection of relational databases holding cultural heritage material from the National Collections of Scotland. The data is a mixture of fixed fields and free text, supported by background material such as domain thesauri. The goal is to produce a system for running queries ...

We present an application of kernel methods to extracting relations from unstructured natural language sources. We introduce kernels defined over shallow parse representations of text, and design efficient algorithms for computing the kernels. We use the devised kernels in conjunction with Support Vector Machine and Voted Perceptron learning algorithms for the task of extracting person-affiliat...