TAC KBP Entity Discovery and Linking - Comprehensive Evaluation Data 2016-2017

Resource

URL

https://dss2.princeton.edu/data/228/

Blurb

Developed by the Linguistic Data Consortium (LDC) and contains training and evaluation data produced in support of the TAC KBP Entity Discovery and Linking (EDL) tasks in 2016 and 2017. It includes queries, knowledge base (KB) links, equivalence class clusters for NIL entities, and entity type information for each of the queries. The EDL reference KB, to which EDL data are linked, is available separately in TAC KBP Entity Discovery and Linking - Comprehensive Training and Evaluation Data 2014-2015 (LDC2019T02). Source documents referenced by the files in this package are available separately in TAC KBP Evaluation Source Corpora 2016-2017 (LDC2019T12).

Text Analysis Conference (TAC) is a series of workshops organized by the National Institute of Standards and Technology (NIST). TAC was developed to encourage research in natural language processing and related applications by providing a large test collection, common evaluation procedures, and a forum for researchers to share their results. Through its various evaluations, the Knowledge Base Population (KBP) track of TAC encourages the development of systems that can match entities mentioned in natural texts with those appearing in a knowledge base and extract novel information about entities from a document collection and add it to a new or existing knowledge base.

The goal of the Entity Discovery and Linking (EDL) track is to conduct end-to-end entity extraction, linking and clustering. For producing gold standard data, given a document collection, annotators (1) extract (identify and classify) entity mentions (queries), link them to nodes in a reference Knowledge Base (KB) and (2) perform cross-document co-reference on within-document entity clusters that cannot be linked to the KB. More information about the TAC KBP EDL task and other TAC KBP evaluations can be found on the NIST TAC website.

Link time

2021-02-24 15:10:00 UTC

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Resource type

Single study

Subjects

• Qualitative Data

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