REFLEX Entity Translation Training/DevTest
|Item Name:||REFLEX Entity Translation Training/DevTest|
|Author(s):||Christopher Walker, Zhiyi Song, Stephanie Strassel, Julie Medero, Kazuaki Maeda|
|LDC Catalog No.:||LDC2009T11|
|Release Date:||March 17, 2009|
|Application(s):||named entity recognition, automatic content extraction|
|Language(s):||English, Mandarin Chinese, Standard Arabic, Arabic|
|Language ID(s):||eng, cmn, arb, ara|
LDC User Agreement for Non-Members
|Online Documentation:||LDC2009T11 Documents|
|Licensing Instructions:||Subscription & Standard Members, and Non-Members|
|Citation:||Walker, Christopher, et al. REFLEX Entity Translation Training/DevTest LDC2009T11. Web Download. Philadelphia: Linguistic Data Consortium, 2009.|
REFLEX Entity Translation Training/DevTest was developed by the Linguistic Data Consortium for the Automatic Contact Extraction (ACE) program. This release constitutes the complete set of training data and development test data for the 2007 REFLEX Entity Translation evaluation sponsored by the National Institute of Standards and Technology (NIST) and consists of approximately 67.5k words of newswire and weblog text for each of three languages: English, Chinese and Arabic. The data set is made up of 22.5k words of English data, 22.5k words of Chinese data, and 22.5k words of Arabic data translated into each of the other two languages and annotated for entities and TIMEX2 extents and normalization.
Entity Annotation. The annotations identify seven types of entities: Person, Organization, Location, Facility, Weapon, Vehicle and GeoPolitical Entity. Each type is further divided into subtypes (for instance, Person subtypes include Individual, Group and Indefinite). Annotators tagged all mentions of each entity within a document, whether named, nominal or pronominal. For every mention, the annotator identified the maximal extent of the string that represents the entity and labeled the head of each mention. Nested mentions were also captured. Each entity was classified according to its type and subtype. Each entity mention was further tagged according to its class such as specific, generic, attributive, negatively quantified or under specified. Annotators also reviewed the entire document to group mentions of the same entity together; they also labeled cases of metonymy, where the name of one entity is used to refer to another entity (or entities) related to it.
TIMEX2 Annotation. TIMEX2 annotation of events and temporal relations fulfills two objectives. The first is the interpretation of expressions that refer to time. Such expressions tell when something happened, or how long something lasted, or how often something occurs. Such expressions also often require knowledge of the temporal context in order to truly understand them. A second objective is the normalization of temporal expressions. This facilitates interoperability between systems. Problems occur, for example, when a programmer in France encodes "October sixteenth 1962" as "1962.10.16" and one in the U.S. encodes it as "10/16/1962". It will appear as if two different dates are being referenced. The standards presented here require that the same meaning is always encoded in the same way.
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