Temporal Processing

The New York Times Annotated Corpus contains over 1.8 million articles written and published by the New York Times between January 1, 1987 and June 19, 2007 with article metadata provided by the New York Times Newsroom, the New York Times Indexing Service and the online production staff at nytimes.com. The corpus includes: - Over 1.8 million articles (excluding wire services articles that appeared during the covered period). - Over 650,000 article summaries written by library scientists. - Over 1,500,000 articles manually tagged by library scientists with tags drawn from a normalized indexing vocabulary of people, organizations, locations and topic descriptors. - Over 275,000 algorithmically-tagged articles that have been hand verified by the online production staff at nytimes.com. As part of the New York Times' indexing procedures, most articles are manually summarized and tagged by a staff of library scientists. This collection contains over 650,000 article-summary pairs which may prove to be useful in the development and evaluation of algorithms for automated document summarization. Also, over 1.5 million documents have at least one tag. Articles are tagged for persons, places, organizations, titles and topics using a controlled vocabulary that is applied consistently across articles. For instance if one article mentions "Bill Clinton" and another refers to "President William Jefferson Clinton", both articles will be tagged with "CLINTON, BILL". Source: https://catalog.ldc.upenn.edu/LDC2008T19

Within the SemEval-2013 evaluation exercise, the TempEval-3 shared task aims to advance research on temporal information processing. It follows on from TempEval-1 and -2, with: a three-part structure covering temporal expression, event, and temporal relation extraction; a larger dataset; and new single measures to rank systems – in each task and in general. We present TempEval-3 Silver data, with 666K words, and TempEval-3 Platinum, an evaluation set with 6K words. Documents are annotation with EVENT and TIMEX3 spans and also TLINKs, following the TimeML standard.

GUM is an open source multilayer English corpus of richly annotated texts from twelve text types. Annotations include: * Multiple POS tags, morphological features and lemmatization * Sentence segmentation and rough speech act * Document structure in TEI XML (paragraphs, headings, figures, etc.) * ISO date/time annotations * Speaker and addressee information (where relevant) * Constituent and dependency syntax * Information status (given, accessible, new, split antecedent) * Entity and coreference annotation, including bridging anaphora * Entity linking (Wikification) * Discourse parses in Rhetorical Structure Theory and discourse dependencies

Enriches the TimeML annotations of TimeBank by adding information about the Topic Time in terms of Klein (1994). The annotations are partly automatic, partly inferential and partly manual. The corpus was converted into the native format of the annotation software GraphAnno and POS-tagged using the Stanford bidirectional dependency network tagger. Source: Enriching TimeBank: Towards a more precise annotation of temporal relations in a text

French TimeBank, a corpus for French annotated in ISO-TimeML. Some statistics: - Documents: 109 - Events: 2100 - Timexs: 608 - Signal: 288 - ALink: 36 - SLink: 457 - TLinks: 191

KRAUTS (Korpus of newspapeR Articles with Underlinded Temporal expressionS) is a German temporally annotated news corpus accompanied with TimeML annotation guidelines for German. It was developed at Fondazione Bruno Kessler, Trento, Italy and at the Max Planck Institute for Informatics, Saarbrücken, Germany. Our goal is to boost temporal tagging research for German.

TimeBankPT is a corpus of Portuguese text with annotations about time. The annotation scheme used is similar to TimeML. TimeBankPT is the result of adapting the English corpus used in the first TempEval challenge to the Portuguese language. In what regards the temporal relaiton type, the corpus was annotated with six labels, namely: BEFORE, AFTER, OVERLAP, BEFORE-OR-OVERLAP, OVERLAP-OR-AFTER and VAGUE. | | Train | Test | |-----------|-------|------| | Documents | 162 | 20 | | Sentences | 2281 | 351 | | Words | 60782 | 8920 | | Events | 6790 | 1097 | | Timexs | 1244 | 165 | | Tlinks | 5781 | 758 |

The Parsing Time Normalizations (PNT) corpus in SCATE format allows the representation of a wider variety of time expressions than previous approaches. This corpus was release with SemEval 2018 Task 6. Source: NLP Progress

Spanish TimeBank 1.0 was developed by researchers at Barcelona Media and consists of Spanish texts in the AnCora corpus annotated with temporal and event information according to the TimeML specification language. TimeML is a schema for annotating eventualities and time expressions in natural language as well as the temporal relations among them, thus facilitating the task of extraction, representation and exchange of temporal information. Spanish Timebank 1.0 is annotated in three levels, marking events, time expressions and event metadata. The TimeML annotation scheme was tailored for the specifics of the Spanish language. Temporal relations in Spanish present distinctions of verbal mood (e.g., indicative, subjunctive, conditional, etc.) and grammatical aspect (e.g., imperfective) which are absent in English. Spanish TimeBank 1.0 joins the family of TimeBank annotated corpora which includes languages such as English, Italian, French, Korean and Chinese. Through their common layer of annotation, these corpora provide resources useful for multilingual temporal extraction and processing, such as multilingual text entailment, opinion mining or question answering. Spanish Timebank 1.0 is the Spanish language complement to Catalan Timebank 1.0 LDC2012T10.

A set of basque documents annotated with EusTimeML - a mark-up language for temporal information in Basque.

Catalan TimeBank 1.0 was developed by researchers at Barcelona Media and consists of Catalan texts in the AnCora corpus annotated with temporal and event information according to the TimeML specification language.

HengamCopus is a Persian corpus with temporal tags (BIO standard tagging scheme). This dataset was generated by applying HengamTagger (https://github.com/kargaranamir/parstdex) to a large number of sentences. There are two types of Persian text datasets included in these collections: formal ones (Persian Wikipedia and Hamshahri Corpus), and informal ones (Twitter and HelloKish). In the creation of HengamCorpus, to maximize the diversity of patterns for training and evaluation, they uniformly draw samples from sets of sentences of unique “temporal pattern profile”, presence/absence vector of different temporal patterns within the sentence.