BOLT Chinese SMS/Chat

Item Name: BOLT Chinese SMS/Chat
Author(s): Song Chen, Dana Fore, Stephanie Strassel, Haejoong Lee, Jonathan Wright
LDC Catalog No.: LDC2018T15
ISBN: 1-58563-847-1
ISLRN: 269-619-825-584-0
DOI: https://doi.org/10.35111/1whw-ea40
Release Date: June 15, 2018
Member Year(s): 2018
DCMI Type(s): Text
Data Source(s): text chat conversations
Project(s): BOLT
Application(s): machine translation
Language(s): Mandarin Chinese
Language ID(s): cmn
License(s): LDC User Agreement for Non-Members
Online Documentation: LDC2018T15 Documents
Licensing Instructions: Subscription & Standard Members, and Non-Members
Citation: Chen, Song, et al. BOLT Chinese SMS/Chat LDC2018T15. Web Download. Philadelphia: Linguistic Data Consortium, 2018.
Related Works: View

Introduction

BOLT Chinese SMS/Chat was developed by the Linguistic Data Consortium (LDC) and consists of naturally-occurring Short Message Service (SMS) and Chat (CHT) data collected through data donations and live collection involving native speakers of Chinese. The corpus contains 14,877 conversations totaling 3,005,810 words across 497,543 messages.

The BOLT (Broad Operational Language Translation) program developed machine translation and information retrieval for less formal genres, focusing particularly on user-generated content. LDC supported the BOLT program by collecting informal data sources -- discussion forums, text messaging and chat -- in Chinese, Egyptian Arabic and English. The collected data was translated and annotated for various tasks including word alignment, treebanking, propbanking and co-reference.

Data

The data in this release was collected using two methods: new collection via LDC's collection platform, and donation of SMS or chat archives from BOLT collection participants. All data collected were reviewed manually to exclude any messages/conversations that were not in the target language or that had sensitive content, such as personal identifying information (PII). All data is presented in UTF-8 XML.

Acknowledgement

This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR0011-11-C-0145. The content does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.

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