LDC December 2023 Newsletter

LDC 2024 membership discounts now available  

Approaching deadline for Spring 2024 data scholarship applications

LDC closed for Winter Break Dec. 25-Jan. 1

New publications:

Kasdi-Merbah (University) Emotional Database in Arabic Speech

TAC-KBP Belief and Sentiment – Comprehensive Training and Evaluation Data 2016-2017
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LDC 2024 membership discounts now available 

Now through March 1, 2024, current 2023 members receive a 10% discount for renewing their membership, and new or returning organizations receive a 5% discount. Membership remains the most economical way to access current and past LDC releases. Consult Join LDC for details on membership options and benefits. 

Approaching deadline for Spring 2024 data scholarship applications

Attention students: don’t miss out on the chance to receive no-cost access to LDC data for your research. Applications for Spring 2024 data scholarships are due January 15, 2024. For more information on requirements and program rules, see LDC Data Scholarships. 

LDC closed for Winter Break Dec. 25-Jan. 1 

LDC will be closed from Monday, December 25, 2023 through Monday, January 1, 2024 in accordance with the University of Pennsylvania Winter Break Policy. Our offices will reopen on Tuesday, January 2, 2024. Requests received by the Membership Office during Winter Break will be processed when the office reopens. 

New publications:
 
Kasdi-Merbah (University) Emotional Database in Arabic Speech was developed by the University of Kasdi Merbah Ouargla and contains two hours of Modern Standard Arabic prompted speech from 500 speakers (254 female, 246 male) representing 5,000 utterances. Each speaker read ten sentences, with two sentences each for five different emotions (sadness, fear, anger, happiness, neutral).

2023 members can access this corpus through their LDC accounts. Non-members may license this data for a fee.

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TAC-KBP Belief and Sentiment – Comprehensive Training and Evaluation Data 2016-2017 includes all training and evaluation data developed by LDC for the Belief and Sentiment tracks: source documents (Chinese, English, and Spanish newswire and discussion forums); gold standard entity, relation, and event annotation; and belief and sentiment annotation.

The goal of the TAC-KBP Belief and Sentiment track was to provide information about beliefs and sentiments held by entities toward other entities, as well as toward events and relations. The gold standard set of labeled entities, relations, and events was used to create a system for automatically labeling belief and sentiment about each possible target (entity, relation or event) and for identifying the entity holding the belief or sentiment. 

2023 members can access this corpus through their LDC accounts. Non-members may license this data for a fee. 

LDC 2020 March Newsletter

Spring 2020 LDC Data Scholarship recipients 
LDC data and commercial technology development

New Publications: 
BOLT Egyptian Arabic-English Word Alignment -- Conversational Telephone Speech Training 

EVALution
Mixer 4 and 5 Speech

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Spring 2020 LDC Data Scholarship recipients 

LDC congratulates the following Spring 2020 Data Scholarship recipients: 

• Zahra Azin (Istanbul Technical University, Turkey) is awarded a copy of Abstract Meaning Representation (AMR) Annotation Release 3.0 (LDC2020T02) for her work in Turkish AMR.
•  Spandan Dey (IIT Kharagpur, India) is awarded a copy of Multi-Language Conversational Telephone Speech – South Asian (LDC2017S14) for his research on automatic language recognition.
• Jonathan Downey (University of California, Santa Barbara, US) is awarded a copy of the ETS Corpus of Non-Native Written English (LDC2014T06) for his research on second language acquisition and quantitative methodologies for educational measurements.
• Nathaniel Fackler (University of Georgia, US) is awarded a copy of the ETS Corpus of Non-Native Written English (LDC2014T06) for his work on adult second language acquisition.
• B. Senthil Kumar (SSN College of Engineering & Anna University, India) is awarded a copy of 2009 CoNLL Shared Task Part 2 (LDC2012T04) for his research on semantic role labeling.
• Ming Li (Colorado School of Mines, US) is awarded a copy of TIDIGITS (LDC93S10) for her research on inferring speech signals from motion data in Internet of Things (IoT) security.
• Jialiang Lin (Xiamen University, China) is awarded a copy of the ETS Corpus of Non-Native Written English (LDC2014T06) for his project to train and test an automated essay scoring model.

Students can learn more about the LDC Data Scholarship program and the next application cycle on the Data Scholarships page. 

LDC data and commercial technology development

For-profit organizations are reminded that an LDC membership is a pre-requisite for obtaining a commercial license to almost all LDC databases. Non-member organizations, including non-member for-profit organizations, cannot use LDC data to develop or test products for commercialization, nor can they use LDC data in any commercial product or for any commercial purpose. LDC data users should consult corpus-specific license agreements for limitations on the use of certain corpora. Visit the Licensing page for further information.

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New publications: 

(1) BOLT Egyptian Arabic-English Word Alignment -- Conversational Telephone Speech Training was developed by LDC and consists of 153,171 words of Egyptian Arabic and English parallel text enhanced with linguistic tags to indicate word relations.

The source data in this release consists of transcripts of Egyptian Arabic conversational telephone speech (CTS) from LDC's CALLHOME and CALLFRIEND collections (LDC97S45, LDC97T19, LDC2002S37, LDC2002T38, LDC96S49) that was translated into English by professional translation agencies and annotated for the word alignment task.

The BOLT word alignment task was built on treebank annotation. Egyptian Arabic source tree tokens were automatically extracted from tree files in LDC’s BOLT Egyptian Arabic Treebank, which had been tagged for part-of-speech and syntactically annotated. That data was then aligned and annotated for the word alignment task.

BOLT Egyptian Arabic-English Word Alignment -- Conversational Telephone Speech Training is distributed via web download. 

2020 Subscription Members will automatically receive copies of this corpus. 2020 Standard Members may request a copy as part of their 16 free membership corpora. Non-members may license this data for a fee. 

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(2) EVALution was developed by The Hong Kong Polytechnic University. It is comprised of English and Mandarin Chinese data sets -- EVALution 1.0 and EVALution-Man, respectively -- that contain semantic relations and metadata for training and evaluating distributional semantic models. 

EVALution 1.0 consists of approximately 7500 English tuples extracted from ConceptNet 5.0 and WordNet 4.0 and filtered through automatic methods and crowd-sourcing. Several semantic relations between word pairs were instantiated, including hypernymy, synonymy, antonymy and meronymy. The corpus also includes additional information that can be used to filter the pairs or to analyze the results, such as relation domain, word frequency, word part-of-speech and word semantic field.

EVALution-MAN consists of Chinese word pairs from two sources: Chinese Wordnet and humans who completed an elicitation task by supplying missing words to sentences. The human-supplied sentence word pairs were then judged by human raters for reliability. 

EVALution is distributed via web download. 

2020 Subscription Members will receive copies of this corpus provided they have submitted a completed copy of the special license agreement. 2020 Standard Members may request a copy as part of their 16 free membership corpora. Non-members may license this data for a fee.

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(3) Mixer 4 and 5 Speech was developed by LDC and contains approximately 14,185 hours of audio recordings of conversational telephone speech, interviews, elicitation exercises and transcript readings involving 616 distinct speakers. The material was collected in 2007 as part of the Mixer project – which supported speaker recognition for a variety of research tasks – and recordings in this corpus were used in the 2008 NIST Speaker Recognition Evaluation.

The data in this release was collected by LDC at its Human Subjects Data Collection Laboratories in Philadelphia and by the International Computer Science Institute (ICSI) at the University of California, Berkeley, as a collaborative, carefully coordinated activity at both recording sites. The Mixer 4 and 5 collection contains 2,568 recordings made via the public telephone network and 2,152 sessions of multiple microphone recordings in office-room settings.

The telephone protocol connected recruited speakers through a robot operator to carry on casual conversations. In Mixer 4, 400 subjects made ten 10-minute calls; half of those subjects also visited one of the collection sites where they made two telephone calls while also being recorded on a cross-channel platform. In Mixer 5, 300 subjects each completed ten calls and six interview sessions at either LDC or ICSI; those sessions were conducted on a cross channel platform and included a telephone call in one of three vocal-effort conditions - normal, high and low. Mixer participants were nearly all native English speakers, the rest being bilingual English speakers.

This release includes metadata about the calls and speakers, along with time-aligned entries for many of the component portions of the recording sessions.

Mixer 4 and 5 Speech is distributed via hard drive.

2020 Subscription Members will automatically receive copies of this corpus. 2020 Standard Members may request a copy as part of their 16 free membership corpora. This corpus is a members-only release and is not available for non-member licensing. Contact ldc@ldc.upenn.edu for information about membership.

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LDC 2020 February Newsletter

Only two weeks left to enjoy 2020 membership discounts

LREC Workshop on Citizen Linguistics - Deadline Extended 

New Publications:

TAC KBP English Event Argument - Training and Evaluation Data 2014-2015 
Chinese CogBank 
Machine Reading Phase 1 ICTraining Data 
IARPA Babel Dholuo Language Pack IARPA-babel403b-v1.0b

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Only two weeks left to enjoy 2020 membership discounts 

There is still time to save on 2020 membership fees. Through March 2, all organizations receive a discount on the 2020 membership fee (up to 10%) when they choose to join or renew. For more information on membership benefits, visit Join LDC.

LREC Workshop on Citizen Linguistics - Deadline Extended

LDC Researchers and their colleagues are organizing a workshop on Citizen Linguistics and Language Resource Development at LREC 2020 (Language Resource and Evaluation Conference) to take place on May 16, 2020. The workshop includes an open call for papers in language-related citizen science, a tutorial on using the new LanguageARC.org citizen linguistics portal and a special session on best papers using LanguageARC. Call for Papers deadline extended until February 24, 2020. _______________________________________________________________________

New publications:  

(1) TAC KBP English Event Argument - Training and Evaluation Data 2014-2015 was developed by LDC and contains training and evaluation data produced in support of the 2014 TAC KBP English Event Argument Extraction Pilot and Evaluation tasks and the 2015 English Event Argument Extraction and Linking Training and Evaluation tasks. 

The Event Argument Extraction and Linking task required systems to extract event arguments (entities or attributes playing a role in an event) from unstructured text, indicate the role they play in an event, and link the arguments appearing in the same event to each other. Since the extracted information must be suitable as input to a knowledge base, systems constructed tuples indicating the event type, the role played by the entity in the event, and the most canonical mention of the entity from the source document. The event types and roles were drawn from an externally-specified ontology of 31 event types, which included financial transactions, communication events, and attacks. 

This corpus includes source documents, manual runs, assessments, and event hoppers, a form of identity coreference for events (2015 only).  Source data is English newswire and discussion forum text collected by LDC. 

TAC KBP English Event Argument - Training and Evaluation Data 2014-2015 is distributed via web download. 

2020 Subscription Members will automatically receive copies of this corpus. 2020 Standard Members may request a copy as part of their 16 free membership corpora. Non-members may license this data for a fee.

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(2) Chinese CogBank is a database of cognitive properties of Chinese words intended for use in metaphor understanding and generation. It consists of 232,497 "word-property" pairs, which are comprised of 83,104 words and 100,195 properties. Each "word-property" type also has an associated frequency which can stand as a functional measure of the importance of a property.

The data was collected via the Chinese search engine Baidu.com. The original collection consisted of 1,258,430 types (5,637,500 tokens) of "word-adjective" pairs that were reduced in Chinese CogBank to 232,497 "word-property" pairs after a series of manual checks.

Chinese CogBank is distributed via web download.

2020 Subscription Members will automatically receive copies of this corpus. 2020 Standard Members may request a copy as part of their 16 free membership corpora. Non-members may license this data for a fee.

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(3) Machine Reading Phase 1 IC Training Data was developed by LDC for use in the DARPA (Defense Advanced Research Projects Agency) Machine Reading program. It contains 248 English source documents and 116 standoff annotation files, annotated with instances of explicit relations and their arguments, as well as some non-explicit relations.

  

The Machine Reading program aimed to develop automated reading systems to bridge the gap between knowledge contained in natural language texts and knowledge accessible to formal reasoning systems. The reading systems designed by program participants were required to extract and reason about facts from text in multiple domains.

  

The data in this release constitutes the training data for the IC (Core Domain) task, which tested the core domain by extracting information about Entities (people, organizations, geopolitical entities) and their involvement in four types of Relations (Attack Relations, Biographical Relations, Affiliation Relations and Family Relations), as described in newswire text. This information was then aligned with an IC Use Cases ontology that would allow automated reasoning about the extracted Entities and Relations. 

Machine Reading Phase 1 IC Training Data is distributed via web download.

2020 Subscription Members will automatically receive copies of this corpus. 2020 Standard Members may request a copy as part of their 16 free membership corpora. Non-members may license this data for a fee.

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(4) IARPA Babel Dholuo Language Pack IARPA-babel403b-v1.0b was developed by Appen for the IARPA (Intelligence Advanced Research Projects Activity) Babel program. It contains approximately 204 hours of Dholuo conversational and scripted telephone speech collected in 2014 and 2015 along with corresponding transcripts. 

The Dholuo speech in this release represents the South Nyanza and Trans-Yala dialect regions of Kenya. The gender distribution among speakers is approximately equal; speakers' ages range from 16 years to 65 years. Calls were made using different telephones (e.g., mobile, landline) from a variety of environments including the street, a home or office, a public place, and inside a vehicle. 

IARPA Babel Dholuo Language Pack IARPA-babel403b-v1.0b is distributed via web download. 

2020 Subscription Members will receive copies of this corpus provided they have submitted a completed copy of the special license agreement. 2020 Standard Members may request a copy as part of their 16 free membership corpora. Non-members may license this data for a fee.

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LDC 2019 September Newsletter

LDC at Interspeech 2019

New Publications:
CALLFRIEND Canadian French Second Edition
BOLT Chinese-English Word Alignment and Tagging -- SMS/Chat Training
Machine Reading Phase 1 NFL Scoring Training Data 

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LDC at Interspeech 2019 

LDC is exhibiting at Interspeech 2019, September 15-19 in Graz, Austria. Stop by Booth F16 to learn more about recent developments at the Consortium and new publications.

Be on the lookout for The Second DIHARD Speech Diarization Challenge (DIHARD II), a special session co-organized by LDC, and the following presentations featuring LDC work: 

The Second DIHARD Diarization Challenge: Dataset - task - and baselines
 Neville Ryant, Christopher Cieri, Mark Liberman (LDC), Kenneth Church (Baidu, USA), Alejandrina Cristia (Laboratoire de Sciences Cognitives et Psycholinguistique), Jun Du (University of Science and Technology of China), Sriram Ganapathy (Indian Institute of Science)
Oral Session, Tuesday September 17, 10:00 – 10:20, Hall 3 

Automatic Detection of Prosodic Focus in American English 
Sunghye Cho and Mark Liberman (LDC), Yong-cheol Lee (Cheongju University)
Poster Session, Wednesday September 18, 16:00 – 18:00, Gallery B 

Automatic detection of ASD in children using acoustic and text features from brief natural conversations 
Sunghye Cho, Mark Liberman, Neville Ryant (LDC), Meredith Cola, Robert T. Schultz, Julia Parish-Morris (Children's Hospital of Philadelphia)
Oral Session, Wednesday September 18, 16:45 – 17:00, Hall 3

LDC will post conference updates via our Twitter feed and Facebook page. We hope to see you there!

New publications: 

(1) CALLFRIEND Canadian French Second Edition was developed by LDC and consists of approximately 26 hours of unscripted telephone conversations between native speakers of Canadian French. This second edition updates the audio files to wav format, simplifies the directory structure and adds documentation and metadata. The first edition is available as CALLFRIEND Canadian French (LDC96S48).

All data was collected before July 1997. Participants could speak with a person of their choice on any topic; most called family members and friends. All calls originated in North America. The recorded conversations last up to 30 minutes. 

CALLFRIEND Canadian French Second Edition is distributed via web download.

2019 Subscription Members will automatically receive copies of this corpus. 2019 Standard Members may request a copy as part of their 16 free membership corpora. Non-members may license this data for a fee.

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(2) BOLT Chinese-English Word Alignment and Tagging -- SMS/Chat Training was developed by LDC for the DARPA BOLT (Broad Operational Language Translation) program and consists of 388,027 words of Chinese and English parallel text enhanced with linguistic tags to indicate word relations.

This release consists of Chinese source text and chat conversations collected using two methods: new collection via LDC's collection platform and donation of SMS and chat archives from BOLT collection participants. The source data is released as BOLT Chinese SMS/Chat (LDC2018T15).

The BOLT word alignment task was built on treebank annotation. LDC automatically extracted Chinese source tokens, including empty categories/traces, from word-segmented files provided by the BOLT Chinese Treebank annotation team at Brandeis University. The word-segmented tokens were then used to automatically generate ctb (Chinese Treebank) alignment, as well as tokenized for character alignment by inserting white spaces to separate characters. 

BOLT Chinese-English Word Alignment and Tagging -- SMS/Chat Training is distributed via web download.

2019 Subscription Members will automatically receive copies of this corpus. 2019 Standard Members may request a copy as part of their 16 free membership corpora. Non-members may license this data for a fee.

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(3) Machine Reading Phase 1 NFL Scoring Training Data was developed by LDC for use in the DARPA (Defense Advanced Research Projects Agency) Machine Reading program. It contains 110 U.S. NFL (National Football League) scoring source documents and 110 standoff annotation files, manually annotated for instances of NFL Scoring annotation categories defined with respect to a NFL Scoring ontology.

The Machine Reading program aimed to develop automated reading systems to bridge the gap between knowledge contained in natural language texts and knowledge accessible to formal reasoning systems. The reading systems designed by program participants were required to extract and reason about facts from text in multiple domains.

The data in this release constitutes the training data for the NFL Scoring Use Cases evaluation, which tested the sports domain by extracting information about scoring events and game outcomes and aligning that information with an NFL Scoring ontology. 

Machine Reading Phase 1 NFL Scoring Training Data is distributed via web download. 

2019 Subscription Members will automatically receive copies of this corpus. 2019 Standard Members may request a copy as part of their 16 free membership corpora. Non-members may license this data for a fee.