Although their main objective was participation only in the Relation Extraction subtask, they also presented tiny [...]]]> Oscar Sainz and Edgar Andrés, students of the HAP-LAP master, obtained an excellent result in the eHealth-2020 challenge presented with professors Oier Lopez de Lacalle and Aitziber Atutxa. Their team (IXA-NER-RE) has been “champion” in the Relational Extraction sub-task.

Although their main objective was participation only in the Relation Extraction subtask, they also presented tiny systems in the other two subtasks (Entity Recognition and Alternative Domain) and so their system was fourth in the main evaluation.

ZORIONAK, CONGRATULATIONS!
You have done a good job!

The results can be consulted here:
https://knowledge-learning.github.io/ehealthkd-2020/results

IXA-NER-RE was the “champion” in “Relation Extraction” subtask

Currently I am a 3rd year PhD student at the University of Sheffield. Before, I studied Computer Science and Computational Linguistics at the University of Trier and Saarland University in Germany. After graduation I worked as a software engineer at the German Research Center for Artificial Intelligence (DFKI). Right now [...]]]> Speaker: Roland Roller

Currently I am a 3rd year PhD student at the University of Sheffield. Before, I studied Computer Science and Computational Linguistics at the University of Trier and Saarland University in Germany. After graduation I worked as a software engineer at the German Research Center for Artificial Intelligence (DFKI). Right now I am visiting your research group for four weeks.

Data: January 28th 2015, Wednesday
Time: 17:00
Room: 3.2 room. Faculty of Informatics (UPV/EHU)

Title: “Biomedical Relation Extraction using Distant Supervision”

Abstract:

Automatic recognition of relationships between key entities in text is an
important task that has been used to identify useful relations such as
interactions between drugs and potential drug side-effects. Supervised
machine learning techniques have proved to be the most effective approach
to this problem. However, they require labelled training data which may not
be available in sufficient quantity (or at all) and is expensive to
produce. Distant supervision is an alternative technique trying to overcome
this problem by labelling large amounts of training data automatically
using known facts from a knowledge base. This talk will present first
results, ongoing research and challenges using distant supervision for
biomedical relation extraction.

Currently, Oier Lopez de Lacalle is a Research Fellow at University of Edinburgh.

Abstract:

In this work we present an unsupervised approach to relational information extraction. Our model partitions tuples representing [...]]]> Last week Oier Lopez de Lacalle participated in our IXA seminar with a presentation entitled “Unsupervised Relation Extraction with General Domain Knowledge“.

Currently, Oier Lopez de Lacalle is a Research Fellow at University of Edinburgh.

Abstract:

In this work we present an unsupervised approach to relational information extraction. Our model partitions tuples representing an observed syntactic relationship between two named entities (e.g., “X was born in Y” and “X is from Y”) into clusters corresponding to underlying semantic relation types (e.g., BornIn, Located). Our approach incorporates general domain knowledge which we encode as First Order Logic rules and automatically combine with a topic model developed specifically for the relation extraction task. Evaluation results on the ACE 2007 English Relation Detection and Categorization (RDC) task show that our model outperforms competitive unsupervised approaches by a wide margin and is able to produce clusters shaped by both the data and the rules.