Multilingual and cross-domain temporal tagging

• Published in: Language Resources and Evaluation Vol. 47; no. 2; pp. 269 - 298
• Main Authors: Strötgen, Jannik, Gertz, Michael
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer 01-06-2013; Springer Netherlands; Springer Nature B.V
• Subjects: Adverbials; Applied linguistics; Computational Linguistics; Computer Science; Corpus linguistics; Data normalization; Domains; Extensibility; Extraction; Format; Information resources; Information retrieval; Language; Language and Literature; Language usage; Languages; Linguistics; Marking; Multilingualism; Narratives; Original Paper; Pipelines; Social Sciences; Source code; Summarization; Syntax; Tasks; Temporal data; Temporal logic; Texts; Wikipedia; TIMEX2; Temporal tagger; Named entity recognition; Named entity normalization; TIMEX3; Temporal information; Corpus annotation; Algorithm; Named entity; English; Tagging; Natural language processing; Computational linguistics
• ISSN: 1574-020X; 1572-8412; 1574-0218
• DOI: 10.1007/s10579-012-9179-y

Extraction and normalization of temporal expressions from documents are important steps towards deep text understanding and a prerequisite for many NLP tasks such as information extraction, question answering, and document summarization. There are different ways to express (the same) temporal information in documents. However, after identifying temporal expressions, they can be normalized according to some standard format. This allows the usage of temporal information in a term- and language-independent way. In this paper, we describe the challenges of temporal tagging in different domains, give an overview of existing annotated corpora, and survey existing approaches for temporal tagging. Finally, we present our publicly available temporal tagger HeidelTime, which is easily extensible to further languages due to its strict separation of source code and language resources like patterns and rules. We present a broad evaluation on multiple languages and domains on existing corpora as well as on a newly created corpus for a language/domain combination for which no annotated corpus has been available so far.