HeidelTime can now also be used for English temponym tagging. For details, see our TempWeb'16 paper.
HeidelTime contains automatically created resources for 200+ languages in addition to manually created ones for 13 languages. For further details, take a look at our EMNLP 2015 paper.
HeidelTime is a multilingual, domain-sensitive temporal tagger developed at the Database Systems Research Group at Heidelberg University. It extracts temporal expressions from documents and normalizes them according to the TIMEX3 annotation standard. HeidelTime is available as UIMA annotator and as standalone version.
HeidelTime currently contains hand-crafted resources for 13 languages: English, German, Dutch, Vietnamese, Arabic, Spanish, Italian, French, Chinese, Russian, Croatian, Estonian and Portuguese. In addition, starting with version 2.0, HeidelTime contains automatically created resources for more than 200 languages. Although these resources are of lower quality than the manually created ones, temporal tagging of many of these languages has never been addressed before. Thus, HeidelTime can be used as a baseline for temporal tagging of all these languages or as a starting point for developing temporal tagging capabilities for them.
HeidelTime distinguishes between news-style documents and narrative-style documents (e.g., Wikipedia articles) in all languages. In addition, English colloquial (e.g., Tweets and SMS) and scientific articles (e.g., clinical trails) are supported.
Want to see what it can do before you delve in? Take a look at our online demo.
A minimal set of dependencies is satisfied by these entries for your pom.xml:
<dependency>
<groupId>org.apache.uima</groupId>
<artifactId>uimaj-core</artifactId>
<version>2.8.1</version>
</dependency>
<dependency>
<groupId>com.github.heideltime</groupId>
<artifactId>heideltime</artifactId>
<version>2.2</version>
</dependency>
For some additional features, you will need to provide additional dependencies. See our Maven wiki page.
If you use HeidelTime, please cite the appropriate paper (in general, this would be the journal paper [4]; if you use HeidelTime with automatically created resources, please cite paper [10]; if you use HeidelTime for temponym tagging, please cite paper [11]):
We want to thank the following researchers for their efforts to develop HeidelTime resources:
Please feel free to use our automatically created resources as starting point, if you plan to manually address a language.
HeidelTime was developed in Java with extensibility in mind -- especially in terms of language-specific resources, as well as in terms of programmatic functionality.