Chinese Lexical Resources for Gender, Number, Animacy
|Item Name:||Chinese Lexical Resources for Gender, Number, Animacy|
|Author(s):||Song Chen, Jiahong Yuan, Xiaoyi Ma, Stephanie Strassel|
|LDC Catalog No.:||LDC2020L02|
|Release Date:||September 15, 2020|
LDC User Agreement for Non-Members
|Online Documentation:||LDC2020L02 Documents|
|Licensing Instructions:||Subscription & Standard Members, and Non-Members|
|Citation:||Chen, Song, et al. Chinese Lexical Resources for Gender, Number, Animacy LDC2020L02. Web Download. Philadelphia: Linguistic Data Consortium, 2020.|
Chinese Lexical Resources for Gender, Number, Animacy was developed by the Linguistic Data Consortium (LDC) and consists of gender, number, and animacy lexicons produced in support of the DARPA DEFT program. Gender, number and animacy are lexical indicators useful for named entity tagging, including the detection of person mentions in text.
DARPA's Deep Exploration and Filtering of Text (DEFT) program aimed to address remaining capability gaps in state-of-the-art natural language processing technologies related to inference, causal relationships and anomaly detection. LDC supported the DEFT program by collecting, creating and annotating a variety of data sources.
This corpus was created by extracting information from newswire texts in Chinese Gigaword Fifth Edition (LDC2011T13) in the following steps: (1) segmenting source documents into sentences; (2) converting any traditional Chinese script to simplified Chinese; (3) tagging all sentences for parts-of-speech; (4) developing queries to detect patterns; and (5) building lexicons based on frequency counts and entity types.
The resulting resources include dictionaries of Chinese animate nominals and names; Chinese nominals and name with gender and number predicted; and other dictionaries of Chinese nominals, names, verbs and pronouns. Each dictionary contains frequency information as well as the features in question.
All lexical data is presented as UTF-8 encoded plain text.
None at this time.
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.