In this project we address the problem of detecting temponyms such as "Clinton's term as secretary of state, the late-2000s financial crisis", inferring their temporal scopes, and mapping them to events in a knowledge base if present there.
We present methods for this kind of temponym resolution, using an entity- and tempex-oriented document model and the Yago knowledge base for distant supervision. We develop a family of Integer linear Programs for jointly inferring temponym mappings to the timeline and knowledge base. This enriches the document representation and also extends the knowledge base by obtaining new alias names for events.
For scientific works about temponyms cite this paper.
In this section we provide HeidelTime temporal tagger with temponym tagging functionality.
HeidelTime is on GitHub:
github.com/HeidelTime/heideltime/
Downloads can be found here:
github.com/HeidelTime/heideltime/releases
If you use HeidelTime for temponym tagging, please cite this paper.
The experimental data can be downloaded here.
The experimental data including the large news corpus used for knowledge base population can be downloaded here (1.4 GB).
If you have any question regarding the data please contact Erdal Kuzey.
EVIN web demo presents the grouping and chaining capabilities of EVIN.
The demo uses a small corpus to show the proof of the concept. Therefore, please check the examples provided in the tutorial. For usage, you can check the video demo.
Watch the video demo showing the functionality of EVIN. The demo paper is here.
EVIN is part of the YAGO-NAGA project at the Max Planck Institute for Informatics in Saarbrücken/Germany. It is developed by the Databases and Information Systems Group.
The main people behind these projects are Erdal Kuzey and Gerhard Weikum.
For questions and comments, please contact Erdal Kuzey.
