Name | DL | Torrents | Total Size | Joe's Recommended Mirror List [edit] | 233 | 8.28TB | 2073 | 0 | Natural Language Processing Datasets (via ResearchRat) [edit] | 25 | 128.02GB | 92 | 0 |
LDC2018T03_tac_kbp_comp_eng_src.tar.zst | 6.80GB |
Type: Dataset
Tags: Dataset, nlp, english, natural language, NIST, corpus, data, text, DARPA, tac, kbp, LDC, corpora
Bibtex:
Tags: Dataset, nlp, english, natural language, NIST, corpus, data, text, DARPA, tac, kbp, LDC, corpora
Bibtex:
@article{, title= {TAC KBP Comprehensive English Source Corpora LDC2018T03}, journal= {}, author= {Joe Ellis and Jeremy Getman and David Graff and Stephanie Strassel}, year= {2018}, url= {https://doi.org/10.35111/3rxw-5114}, doi= {10.35111/3rxw-5114}, isbn= {1-58563-836-6}, islrn= {413-253-098-648-6}, ldc= {LDC2018T03}, dcmi= {text}, projects= {tac}, languages= {English}, applications= {information extraction, knowledge base population, knowledge representation}, abstract= {# TAC KBP Comprehensive English Source Corpora 2009-2014 See also the [training and evaluation data](https://academictorrents.com/details/69d99dffac95e764bdf4bd21bef1c059116cbc3e). # Introduction TAC KBP Comprehensive English Source Corpora 2009-2014 was developed by the Linguistic Data Consortium (LDC) and contains the 3,877,207 English source documents used in support of the TAC KBP tasks from 2009-2014. Text Analysis Conference ([TAC](https://tac.nist.gov/)) is a series of workshops organized by the National Institute of Standards and Technology ([NIST](https://www.nist.gov/)). 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. # Data The source data consists of newswire, broadcast material, and web text collected by LDC. Documents are released as a collection of zip files for overall compactness, and ease and efficiency of use. When unpacked the documents are all UTF-8 text files with a basic markup structure. Also provided are a series of lists and tables to aid in specific zip file to doc mappings and the recreation of specific test sets. See the included documentation for more information. # Acknowledgement This material is based on research sponsored by Air Force Research Laboratory and Defense Advance Research Projects Agency under agreement number FA8750-13-2-0045. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Air Force Research Laboratory and Defense Advanced Research Projects Agency or the U.S. Government. # Samples Please view this [sample](https://catalog.ldc.upenn.edu/desc/addenda/LDC2018T03.txt). # Metadata - Item Name: TAC KBP Comprehensive English Source Corpora 2009-2014 - Author(s): Joe Ellis, Jeremy Getman, David Graff, Stephanie Strassel - LDC Catalog No.: LDC2018T03 - ISBN: 1-58563-836-6 - ISLRN: 413-253-098-648-6 - DOI: https://doi.org/10.35111/3rxw-5114 - Release Date: February 16, 2018 - Member Year(s): 2018 - DCMI Type(s): Text - Data Source(s): newswire, discussion forum, newsgroups, weblogs - Project(s): TAC - Application(s): information extraction, knowledge base population, knowledge representation - Language(s): English - Language ID(s): eng - Online Documentation: LDC2018T03 Documents - Citation: Ellis, Joe, et al. TAC KBP Comprehensive English Source Corpora 2009-2014 LDC2018T03. Web Download. Philadelphia: Linguistic Data Consortium, 2018. }, keywords= {Dataset, nlp, english, natural language, NIST, corpus, data, text, DARPA, tac, kbp, LDC, corpora}, terms= {}, license= {}, superseded= {} }