README FILE FOR LDC CATALOG ID: LDC2020TNN
TITLE: LORELEI Kinyarwanda Incident Language Pack
AUTHORS: Jennifer Tracey, Dave Graff, Stephanie Strassel, Michael Arrigo,
Jonathan Wright, Ann Bies
1.0 Introduction
This corpus contains all the text data, annotations and supplemental resources
for the Kinyarwanda language that were used in the DARPA LORELEI / LoReHLT 2018
Evaluation, which was conducted by NIST in August of that year.
Detailed information about the corpus content is provided in section 3
for each of the partitions ("sets") in the corpus. Combining all sets,
the corpus contains approximately 11.9 million words of monolingual text
in Kinyarwanda, 35,000 words of monolingual text in English, 3.4 million words
of parallel Kinyarwanda-English text, and 50,000 words each of English and
Kinyarwanda data annotated for Entity Discovery and Linking and Situation Frames.
The LORELEI (Low Resource Languages for Emergent Incidents) Program is
concerned with building Human Language Technology for low resource
languages in the context of emergent situations like natural disasters
or disease outbreaks. Linguistic resources for LORELEI include
Representative Language Packs for over 2 dozen low resource languages,
comprising data, annotations, basic natural language processing tools,
lexicons and grammatical resources. Representative languages are
selected to provide broad typological coverage, while Incident
Languages are selected to evaluate system performance on a language
whose identity is disclosed at the start of the evaluation, and for
which no training data has been provided. This package comprises all
of the resources and test set references for Kinyarwanda, which was one of
the Program's Incident Languages.
The evaluation protocol is based on a scenario in which some unforeseen event
(the "incident") triggers a need for humanitarian and logistical support in a
region where the predominant language (the "incident language") is one that
has received little or no attention as yet in NLP research. The objective for
evaluation participants is to provide NLP solutions, including information
extraction and machine translation, based only on limited resources and with
very little time for development.
For more information about LORELEI language resources, see
https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/lrec2016-lorelei-language-packs.pdf.
Each incident language pack has one or more focal incidents (a natural
disaster or other event which might trigger humanitarian needs). To
support the evaluation scenario, the evaluation package contents are
divided into the following subsets:
set0 : "pre-incident" text data and reference resources for the
language, including monolingual text, dictionaries, grammars,
and parallel or comparable text (in English and the incident
language); monolingual and parallel data in this set includes
documents published prior to the beginning of the earliest
focal incident and/or reference materials for which
publication date is not relevant, such as religious
materials
setE : "post-incident" text data that forms the basis for scoring
NLP system performance (using the scoring protocol and
software developed by NIST); set E consists of monolingual
text, along with reference translations and annotations
setS : "post-incident" text data in English, including information
that pertains to the incident itself; this was made available
to systems after the initial set of scorable outputs had been
submitted
set1 : supplemental "post-incident" text data, made available after
the initial set of scorable outputs had been submitted
Each subset is presented as a directory at the top-level of the
release package. Tools for data processing are provided as part of
set0 only, but are applicable to all sets.
2.0 Corpus organization
2.1 Directory Structure
The directory structure and contents of the package are summarized
below -- paths shown are relative to the base (root) directory of the
package:
./docs/README.txt -- this file
./set0/
./set0/tools/ -- software for data file format conversion
./set0/data/ -- monolingual and parallel text directories
./set0/dtds/ -- DTDs for all .xml data formats
./set0/docs/ -- lexical and grammatical resources, information
about various set0 components and properties
./set1/data/ -- monolingual text
./set1/docs/ -- information about various set1 components and properties
./setS/data/ -- monolingual text
./setS/docs/ -- information about various setS components and properties
./setE/data/monolingual_text/ -- monolingual text directories for eng and il9
./setE/data/annotation/il9
il9_edl.tab -- table of Entity-Detection-Linking annotations on il9 documents
situation_frame/ -- subdirectories for entity mentions, needs,
and issues tables for il9 documents
twitter_tokenization/ -- ltf.xml files for annotated tweets with
underscore characters ('_') in place of all non-white-space
characters so that users have access to the token boundaries for
annotated tweets
./setE/data/annotation/eng
eng_il9_edl.tab -- table of Entity-Detection-Linking annotations on English documents
situation_frame/ -- subdirectories for entity mentions, needs, and issues tables for English documents
twitter_tokenization/ -- ltf.xml files for annotated tweets with
underscore characters ('_') in place of all non-white-space
characters so that users have access to the token boundaries for
annotated tweets
./setE/data/translation/
eng/
ltf/ -- ltf.xml files
psm/ -- psm.xml files
il9/
ltf/ -- ltf.xml files
psm/ -- psm.xml files
./setE/docs/ -- information about various setE components and properties
2.2 File Name Conventions
All monolingual text documents are presented as distinct files with unique
file names. For convenience, each file name provides a consistent set of
information about the content of the file via a set of fixed-width fields,
as follows:
- Language (3 letters)
- Genre (2 letters)
- Source (6-digit numeric)
- Date (8-digit numeric)
- Unique Index Number (9 alpha-numeric characters)
The language field for all Kinyarwanda documents uses "IL9" instead of the
ISO code for the language, as the practice in LORELEI was to refer to
incident languages by numeric identifiers to preserve the secrecy of
the language name until the start of the evaluation.
The date field for news reports represents the date of original publication
for the report. Where possible, discussion forum material uses the date when
a given discussion thread was initiated. When date information is not
available or meaningful for a given document, the date field will reflect
(roughly) the time at which the content was initially collected by the LDC,
and may be left "incomplete" by setting the "day" field (last two digits) to
zero (e.g. "20140900").
Files containing translations from a source language have the source language
identified in the "Language Code" field of the file name, and the translation
language as a 3-letter extension that immediately follows the main part of the
file name.
Pairs of corresponding files in "found" translation may have distinct
identifier strings (one with IL9 in the initial file name field, and one with
ENG in that field), if they were harvested independently of each other and
were later found to contain parallel content. Alternately, some sources of
found translation data present their own source and translated text as a
single unit, in which case the corresponding pair of files will have a single
identifier string, and the English member of the pair will have ".eng"
appended. In the former case, the alignment data specifies how the IL9 and
ENG files are paired.
2.3 Genres
Five genres are represented in this data set, as follows:
NW - news and general text harvested from news sites
SN - "social network" data (i.e. Twitter)
WL - weblog and newsgroup data
DF - discussion forum data
RF - data from "reference" materials, including religious text, government/NGO information sites, etc.
Note that the SN (Twitter) data cannot be distributed directly by LDC, due to
the Twitter Terms of Use. The file "docs/twitter_info.tab" (described in
Section 6.0 below) provides the necessary information for users to fetch the
particular tweets directly from Twitter.
3.0 Content Summary
3.1 Set 0
3.1.1 Monolingual text
Document and token counts of monolingual text by genre:
Genre N_Docs N_Tokens
NW 28,990 9,316,002
WL 1,035 907,048
SN 133 2,024
3.1.2 Parallel and comparable text
Parallel text document and token count by genre (counts based on
Kinyarwanda documents):
Genre N_Docs N_Tokens
RF 5101 3,392,326
All parallel text is aligned at the sentence level. Parallel text for
Kinyarwanda and English can be found in set0/data/translation/, which
contains the following structure of subdirectories:
found/
sentence_alignment/
eng/{ltf,psm}/
il9/{ltf,psm}/
The "found" data set consists of files from web data sources that had
parallel text content in Kinyarwanda and English. Each "leaf" directory in
the tree (*/ltf, */psm, sentence_alignment) contains a matched set of
data files. Parallel file pairs were identified and harvested
automatically, processed into LTF.xml format, and then aligned at the
level of "segments" (putative sentences). The alignment files
(*.aln.xml) contain one or more "alignment" elements, in which one
or more "source" (English) segments is associated with one or more
"translation" (Kinyarwanda) segments. It's not assured that all segments in
a given (Kinyarwanda or English) data file are accounted for in a given set
of alignments. The sentence alignment files contain references to the
source document and the translation document (both files can be found
in their respective directories), and multiple "alignment" elements,
each of which contains one source element and one translation element.
The "segments" attribute of the source and translation element
contains space delimited segment ids referring to SEG IDs in the
corresponding ltf files.
NB: We refer to English as the "source" purely as a matter of
convenience and consistency across language packs; we do not have
confirmable evidence as to the true original language of a given data
file. In fact, for some web data sources, it may be the case that
documents were translated from some third language into both English
and Kinyarwanda.
3.1.3 Lexical and grammatical resources
The docs/ directory contains two subdirectories:
categoryI_dictionary/
This directory contains the file IL9_dictionary.txt, which
is a parallel English-Kinyarwanda wordlist compiled by LDC, and
a file called IL9_CategoryI_dictionaryinfo.pdf, which provides
pointers to additional bilingual dictionaries available online.
categoryII/
LORELEI Incident Language packs were required to contain (pointers
to) at least 5 of the following 8 "category II" resources:
-- bilingual IL-non-English dictionary
-- monolingual IL dictionary
-- bilingual grammar (reference grammar of the IL in English)
-- monolingual grammar in the IL
-- monolingual primer (grammar in the IL of the type used by school children)
-- bilingual gazetteer
-- monolingual gazetteer in the IL
-- monolingual gazetteer in English covering the incident region
The categoryII directory contains a pdf file (CategoryII_list.pdf)
with additional information and URLs for the resources identified. The
bilingual_gazetteer.txt is from Geonames (www.geonames.org) and is a
gazetteer for the country of Rwanda.
3.2 Set 1
All data in this set is monolingual text in Kinyarwanda from the date of the
incident that serves as the focus of the evaluation and later. It may
contain some information about the incident, but also contains
documents whose content is not relevant to the incident in any way.
Genre N_Docs N_Tokens
NW 4,316 1,386,530
WL 160 140,944
SN 1,991 29,227
3.3 Set S
All data in this set is monolingual text in English from the date of
the incident that serves as the focus of the evaluation and later. It
may contain some information about the incident, but also contains
documents whose content is not relevant to the incident in any way.
Genre N_Docs N_Tokens
NW 45 20,071
WL 21 17,186
3.4 Set E
3.4.1 Monolingual Text
This data set provides monolingual source data for the LORELEI 2018
Evaluation Test Sets in Kinyarwanda and English. All data in this set
is from the date of the incident that serves as the focus of the
evaluation and later.
Kinyarwanda
Genre N_Docs N_Tokens
NW 389 100,841
WL 81 51,103
SN 1,535 22,987
English
Genre N_Docs N_Tokens
NW 37 20,488
WL 20 12,754
SN 857 15,995
Because annotations obey the "full-token rule", meaning that all
reference annotation extents coincide with token boundaries as
provided by the automatic tokenization process, it was deemed to be
important for participants in the evaluation to be able to match the
LDC's tokenization for Twitter documents that they retrieved directly
from the Twitter API. For this reason, in set E only, the
annotation/twitter_tokenization directory contains "scrubbed" ltf for Twitter
documents. These ltf documents contain none of the actual tweet
content, but instead contain a series of underscores and whitespace
which allow users to match the tokenization of the tweet via the
character offsets provided in the ltf file.
3.4.2 Translation
Human reference translations were provided for a subset of the data in
the test set.
Genre N_Docs N_Tokens
NW 124 38,531
WL 25 13,189
The translation/ directory under setE/data/ contains source and
reference translation files, as follows:
il9/{ltf,psm}/ -- contain 149 ltf/psm pairs
eng/{ltf,psm}/ -- contain 149 ltf/psm pairs
3.4.3 Annotation
Entity Detection and Linking and Situation Frame annotations were
applied to a subset of the data, in order to identify "entities",
"needs" and "issues" to be detected by systems for scoring purposes:
Genre N_Docs_IL9 N_Docs_ENG
NW 71 37
SN 1,011 857
WL 25 21
total 1,107 915
Some of the files that received annotation did not yield annotatable
content for one or more annotation types.
The annotation/il9/ and annotation/eng/ directories under setE/data/
each contain tab delimited files "il9_edl.tab" and "eng_il9_edl.tab",
respectively, containing the entity linking annotation. They also
contain a set of directories containing situation frame annotation as
follows:
situation_frame/ -- contains subdirectories for each type:
issues/
mentions/
needs/
Situation Frame annotation is designed to extract basic information
about where needs (such as a need for food) and relevant issues (such
as civil unrest) exist; the information is designed to be of the type
that would be useful for planning a disaster response effort. For
more detailed information about situation frame annotation, see
https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/smerp2017.pdf.
Guidelines for both the EDL and Situation Frame tasks are included in
the docs/ directory of set0.
4.0 Data Formats
The data formats described below are common across all sets.
4.1 PSM - Primary Structural Markup
When original data has structural markup interleaved with the language
content, we apply a filtering process that, in effect, separates the markup
and language content into distinct files. The language content (with
white-space normalization) goes into an RSD file (see below), and the relevant
markup content goes into a corresponding PSM file, which is a simple XML
stream comprising tags with attributes, and no other text content of its own.
(Configuring the filter for a given data source involves determining which
content and markup are "relevant"; the filter eliminates other content and
markup as irrelevant, such as ads, navigation menus, etc.)
Each PSM file has a single "psm" tag as its root element, and contains one
or more "string" tags. Each "string" refers to some span of text in the
corresponding RSD file, using "begin_offset" and "char_length" attributes, and
assigns a label to it, using a "type" attribute. (Note that offsets and
lengths are expressed as Unicode CHARACTER counts, not byte counts.)
The "type" attribute tells what sort of markup tag was used in the original
data to contain the given string (e.g. "p", "quote", etc.); when sentence
segmentation can be done as part of the filtering step, a "string" tag with
type="seg" is used to label the span of each detected sentence.
Some structural tags in original data contain attributes that may be relevant
to language research; for example, in a file that contains a thread from a
discussion forum, it's useful to keep track of the dates and authors of posts
within the thread. For these cases, the "string" element can contain one ore
more "attribute" elements, to preserve the name and value of the given
attribute - e.g.:
As shown in this example, the "attribute" tag is also used, where appropriate,
to assign an ID value (unique within the file) to each string of a given type;
this is also used with the "seg"-type strings to assign IDs to detected
sentences.
PSM files appear in the data/monolingual_text/ and data/translation/
directories of each set.
4.2 LTF - LORELEI Text Format
LTF was originally developed for language packs produced in the REFLEX Program
("LCTL Text Format"). This XML format uses structural tags "SEG" and "TOKEN"
to mark sentence segmentation and word tokenization of the source data. The
full original text of each sentence (SEG) is contained in an "ORIGINAL_TEXT"
tag, and each individual word and punctuation string is contained, in order of
occurrence, in a sequence of "TOKEN" elements, along with various attributes
for each token. Both SEG and TOKEN attributes include character offsets
relative to beginning of the raw source data ("RSD" file format, described
below), with the offset of the first character being 0.
LTF files appear in the data/monolingual_text/ and data/translation/ (where
applicable) directories of each set.
4.3 EDL (Entity Detection and Linking)
The edl.tab files contains all EDL annotations for the EDL task.
The table contains eight columns, as follows:
column 1: system_run_id -- "LDC"
column 2: mention_id
column 3: mention_text
column 4: extents
column 5: kb_id -- numeric-ID or "NIL"+numeric, may contain multiple KB links
separated by | ("pipe" symbol)
column 6: entity_type
column 7: mention_type
column 8: confidence
When column 5 is fully numeric, it is a citation to a numbered entity in the
Reference Knowledge Base (distributed separately as LDC2020T10); when it
consists of "NIL" plus digits, it refers to an entity that is not present in
the Knowledge Base, but this label is used consistently for all mentions of
the particular entity.
Note that for any annotated Twitter documents, text extents have been
replaced by underscore ("_") characters to comply with the prohibition
against distributing the text of tweets directly. Character offsets
can be used to align the annotations with the tweets once the user has
downloaded them using Twitter's API.
4.4 Situation Frame
Situation frame annotation consists of three parts, each presented as a
separate tab-delimited file: entities, needs, and issues. The details of each
table are described below.
Entities, mentions, need frames, and issue frames all have IDs that follow a
standard schema consisting of a prefix designating the type of ID ('Ent' for
entities, 'Men' for mentions, and 'Frame' for both need and issue frames), an
alphanumeric string identifying the annotation "kit", and a numeric string
uniquely identifying the specific entity, mention, or frame within the
document.
4.4.1 Entities
The grouping of entity mentions into "selectable entities" for situation frame
annotation is provided in the mentions/ subdirectory. The table has 8 columns
with the following headers and descriptions:
column 1: doc_id -- doc ID of source file for the annotation
column 2: entity_id -- unique identifier for each grouped entity
column 3: mention_id -- unique identifier for each entity mention
column 4: entity_type -- one of PER, ORG, GPE, LOC
column 5: mention_status -- 'representative' or 'extra';
representative mentions are the ones which have been chosen by the
annotator as the representative name for that entity. Each entity
has exactly one representative mention.
column 6: start_char -- character offset for the start of the mention
column 7: end_char -- character offset for the end of the mention
column 8: mention_text -- mention string
Again, note that for any annotated Twitter documents, text extents
have been replaced by underscore ("_") characters to comply with the
prohibition against distributing the text of tweets directly.
4.4.2 Needs
Annotation of need frames is provided in the needs/ subdirectory. Each row in
the table represents a need frame in the annotated document. The table has 15
columns with the following headers and descriptions:
column 1: user_id -- user ID of the annotator
column 2: doc_id -- doc ID of source file for the annotation
column 3: frame_id -- unique identifier for each frame
column 4: frame_type -- 'need'
column 5: need_type -- exactly one of 'evac' (evacuation), 'food' (food
supply), 'search' (search/rescue), 'utils' (utilities, energy, or
sanitation), 'infra' (infrastructure), 'med' (medical assistance),
'shelter' (shelter), or 'water' (water supply)
column 6: place_id -- entity ID of the LOC or GPE entity identified as the
place associated with the need frame; only one place value per
need frame, must match one of the entity IDs in the corresponding
ent_output.tsv or be 'none' (indicating no place was named)
column 7: proxy_status -- 'True' or 'False'
column 8: need_status -- 'current', 'future'(future only), or 'past' (past only)
column 9: scope -- '1_smallgroup', '2_largegroup', '3_municipality', '4_region',
or 'none'
column 10: severity -- '1_discomfort', '2_injury', '3_possibledeath', '4_certain
death', or 'none'
column 11: resolution_status -- 'sufficient' or 'insufficient' (insufficient /
unknown sufficiency)
column 12: reported_by -- entity ID of one or more entities reporting
the need; multiple values are comma-separated, must match entity IDs
in the corresponding ent_output.tsv or be 'none'
column 13: resolved_by -- entity ID of one or more entities resolving
the need; multiple values are comma-separated, must match entity IDs
in the corresponding ent_output.tsv or be 'none'
column 14: description -- string of text entered by the annotator as
memory aid during annotation, no requirements for content or language,
may be 'none'
column 15: kb_id -- numeric-ID or "NIL"+numeric, may contain multiple KB links
separated by | ("pipe" symbol)
4.4.3 Issues
Annotation of issue frames is provided in the issues/ subdirectory. Each row
in the table represents an issue frame in the annotated document. The table has
9 columns with the following headers and descriptions:
column 1: user_id -- user ID of the annotator
column 2: doc_id -- doc ID of source file for the annotation
column 3: frame_id -- unique identifier for each frame
column 4: frame_type -- 'issue'
column 5: issue_type -- exactly one of 'regimechange' (regime change),
'crimeviolence' (civil unrest or widespread crime), or 'terrorism'
(terrorism or other extreme violence)
column 6: place_id -- entity ID of the LOC or GPE entity identified as
the place associated with the issue frame; only one place value per
issue frame, must match one of the entity IDs in the corresponding
ent_output.tsv or be 'none'
column 7: proxy_status -- 'True' or 'False'
column 8: issue_status -- 'current' or 'not_current'
column 9: scope -- '1_smallgroup', '2_largegroup', '3_municipality', '4_region',
or 'none'
column 10: severity -- '1_discomfort', '2_injury', '3_possibledeath', '4_certain
death', or 'none'
column 11: description -- string of text entered by the annotator as
memory aid during annotation, no requirements for content or
language, may be 'none'
column 12: kb_id -- numeric-ID or "NIL"+numeric, may contain multiple KB links
separated by | ("pipe" symbol)
5.0 Software tools included in this release
All software tools are provided in the tools/ directory of Set 0.
5.1 "ltf2txt" (source code written in Perl)
A data file in ltf.xml format (as described above) can be conditioned
to recreate exactly the "raw source data" text stream (the rsd.txt
file) from which the LTF was created. The tools described here can be
used to apply that conditioning, either to a directory or to a zip
archive file containing ltf.xml data. In either case, the scripts
validate each output rsd.txt stream by comparing its MD5 checksum
against the reference MD5 checksum of the original rsd.txt file from
which the LTF was created. (This reference checksum is stored as an
attribute of the "DOC" element in the ltf.xml structure; there is also
an attribute that stores the character count of the original rsd.txt
file.)
Each script contains user documentation as part of the script content;
you can run "perldoc" to view the documentation as a typical unix man
page, or you can simply view the script content directly by whatever
means to read the documentation. Also, running either script without
any command-line arguments will cause it to display a one-line
synopsis of its usage, and then exit.
ltf2rsd.perl -- convert ltf.xml files to rsd.txt (raw-source-data)
ltfzip2rsd.perl -- extract and convert ltf.xml files from zip archives
5.2 "twitter-processing" (source code written in Ruby)
Due to the Twitter Terms of Use, the text content of individual tweets
cannot be redistributed by the LDC. As a result, users must download
the tweet contents directly from Twitter and condition/normalize the
text in a manner equivalent to what was done by the LDC, in order to
reproduce the Kinyarwanda raw text that was used by LDC for annotation.
The twitter-processing software provided in the tools/ directory
enables users to perform this normalization and ensure that the user's
version of the tweet matches the version used by LDC, by verifying
that the md5sum of the user-downloaded and processed tweet matches the
md5sum provided in the twitter_info.tab file. Users must have a
developer account with Twitter in order to download tweets, and the
tool does not replace or circumvent the Twitter API for downloading
tweets.
The twitter_info.tab file provides the twitter download id for each
tweet, along with the LORELEI file name assigned to that tweet and the
md5sum of the processed text from the tweet.
The file "README.md" in the tools/twitter-processing/ directory
provides details on how to install and use the source code in this
directory in order to condition text data that the user downloads
directly from Twitter and produce both the normalized raw text and the
segmented, tokenized LTF.xml output.
5.3 Encoding
The common framework for text processing in LORELEI includes a
“normalization” step, which allows for rectifying variations in
orthography and/or punctuation that may occur with some frequency in
this or that particular language. For overall simplicity and
consistency in processing across all languages, this normalization
step is always invoked; in languages that require no
special normalization, this step leaves the data unchanged.
6.0 Documentation included in this release
Each set has its own docs directory, but the types of files found
there are consistent across the sets, as described below.
IL9_incident_description.pdf:
provides information about the incidents that were the focus of the
evaluation data set. Found in set0/docs/ only.
SimpleNamedEntityGuidelines_IL9_V1.0.pdf,
EntityLinkingGuidelines_V1.3.pdf,
Entity_NAM-NOM_Annotation_Guidelines_English_V2.0.pdf and
SituationFrameGuidelines_V4.0.pdf:
guidelines for entity annotation (including nominal entities for
English only), entity linking, and situation frame annotation. Found
in set0/docs/ only.
twitter_info.tab:
contains tab-separated columns: doc uid, tweet id, normalized md5 of
the tweet text, and tweet author id for all tweets in the
release. Found in all sets (except set S, which contains no Twitter
data).
source_codes.tab:
contains tab-separated columns: genre, source code, source name, and
base url for each source in the release. Found in all sets.
urls.tab:
contains tab-separated columns: doc uid and url. Note that the url
column is empty for documents from older releases for which the url is
not available; they are included here so that the uids column can
serve as a document list for the package. Found in all sets.
eng_il9_annotated_filelist.txt and il9_annotated_filelist.txt:
list of all files annotated for the EDL and Situation Frame
tasks. Found in setE only.
7.0 Known Issues
Parallel text alignment of the found parallel text in set 0 has a
higher than expected rate of errors in the sentence level alignment.
8.0 Acknowledgement
The authors would like to acknowledge the following contributors to
this corpus: Song Chen, Dana Delgado, Neville Ryant, Brian Gainor,
Neil Kuster, University of Maryland Applied Research Laboratory for
Intelligence and Security (ARLIS), formerly UMD Center for Advanced
Study of Language (CASL), and our team of Kinyarwanda annotators.
This material is based upon work supported by the Defense Advanced Research
Projects Agency (DARPA) under Contract No. HR0011-15-C-0123. Any opinions,
findings and conclusions or recommendations expressed in this material are
those of the author(s) and do not necessarily reflect the views of DARPA.
9.0 Copyright
Portions © 2017-2018 24.com, © 2018 ABC, © 2017-2018 Africanews, ©
2017 AFRICAN PEOPLE'S VOICE, © 2016 AKARERE KA GATSIBO, © 2018 AKARERE
KA GICUMBI, © 2018 AKARERE KA NGORORERO, © 2018 AKARERE KA NGOMA, ©
2016 Al Jazeera Media Network, © 2017-2018 AllAfrica, © 2015-2017
Ayyaantuu.org, © 2018 BBC, © 2016 bilisummaa, © 2019 bwiza, © 2017
Cable News Network. Turner Broadcasting System, Inc., © 2017-2018
Ebenezer Media Group Ltd, © 2017 eNews Channel Africa, © 2017-2018
euronews, © 2015-2016 FloodList, © 2016 France 24, © 2016 Free RWANDA,
© 2015 Guardian News and Media Limited or its affiliated companies, ©
2015-2018 IGIHE Ltd, © 2016 Independent Media and affiliated
companies, © 2016 ingenzinyayo.com, © 2018 The inspirer Ltd, ©
2016-2017 Intara y'Iburasirazuba, © 2016, 2018 Intyoza, © 2016
Inyarwanda Ltd, © 2015-2016, 2018 INYENYERI NEWS, © 2017 ISANGO STAR,
© 2015-2016 Izuba Rirashe, © 2016 Karen Organization of Minnesota, ©
2011-2018 Kigali Today, © 2015-2017 KT PRESS, © 2017 Mail & Guardian
Online, © 2014-2018 Makuruki, © 2015 MARYLAND DENTAL ACTION COALITION,
© 2015 MDA, © 2016 Ministry of Disaster Management and Refugee Affairs
(MIDIMAR), © 2015-2017 Ministry of Foreign Affairs of Ukraine, ©
2017-2018 Mount Carmel Health System, © 2018 Muhabura.com, © 2016-2017
Nation Media Group,© 2017-2018 Nationwide Children’s Hospital, © 2017
New Vision, © 2017 New York Times, © 2017-2018 OhioHealth, © 2018 Okay
Africa, © 2016 Panorama, © 2016 paxpress.org, © 2018 Philadelphia
Media Network (Digital), LLC, © 2017 Quartz Media LLC, © 2018 RBA, ©
2018 RDN LTD, © 2016-2017 Rugali, © 2016 Rwanda News Agency, © 2018
Scientific American, a Division of Nature America, Inc., © 2015 Siloam
Health, © 2018 SMW Communications LTD, © 2017 Tele10 Group, ©
2016-2017 The National Radio Company of Ukraine, © 2016 The New Times
Rwanda, © 2017-2018 The Ohio State University, © 2018 tiso blackstar
group (Pty) Ltd, © 2017-2018 TouchRwanda.com, © 2018 TURKUVAZ
COMMUNICATION AND PUBLICATION CORPORATION, © 2018 TV1, © 2014 - 2017
Ubukungu, © 2016 UMUCYO.rw, © 2016 umurashi.rw,© 2016, 2018 Umuryango,
© 2017 Umwezi.net, © 2011-2017 Umuseke, © 2015 University of
Louisville, © 1995-2018 University of Washington, © 2015
U.S. Committee for Refugees and Immigrants, © 2010-2018 Watch Tower
Bible and Tract Society of Pennsylvania,© 2017-2018 Wexner Medical
Center, © 2016 XINHUANET.com, © 2018 Yeejo Limited,© 2018, 2020
Trustees of the University of Pennsylvania
10.0 Contacts
If you have questions about this data release, please contact the
following personnel at LDC.
Stephanie Strassel - LORELEI PI
Jonathan Wright - LORELEI Technical Lead