EMNLP
E736213
EMNLP (Empirical Methods in Natural Language Processing) is a leading annual conference in computational linguistics and natural language processing research.
All labels observed (6)
| Label | Occurrences |
|---|---|
| EMNLP canonical | 5 |
| Conference on Empirical Methods in Natural Language Processing | 2 |
| EMNLP (Conference on Empirical Methods in Natural Language Processing) | 1 |
| EMNLP 2014 | 1 |
| EMNLP conferences | 1 |
| EMNLP workshops | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T8483058 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: EMNLP Context triple: [Nal Kalchbrenner, hasGivenTalkAt, EMNLP]
-
A.
ACM Transactions on Asian and Low-Resource Language Information Processing
ACM Transactions on Asian and Low-Resource Language Information Processing is a peer-reviewed scholarly journal focusing on computational linguistics, natural language processing, and information processing for Asian and other low-resource languages.
-
B.
NeurIPS
NeurIPS is a premier international conference focused on advances in machine learning, artificial intelligence, and computational neuroscience.
-
C.
NIPS
NIPS is the acronym for the Northern Ireland Prison Service, the government agency responsible for managing prisons and overseeing the custody and rehabilitation of offenders in Northern Ireland.
-
D.
CSLI Publications
CSLI Publications is an academic publishing house associated with Stanford University's Center for the Study of Language and Information, specializing in linguistics, cognitive science, philosophy, and related fields.
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E.
“A Computer Program for Understanding Natural Language”
“A Computer Program for Understanding Natural Language” is a landmark 1968 paper by Terry Winograd that presents an early natural language understanding system capable of interpreting and executing commands in a simulated blocks world.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: EMNLP Target entity description: EMNLP (Empirical Methods in Natural Language Processing) is a leading annual conference in computational linguistics and natural language processing research.
-
A.
ACM Transactions on Asian and Low-Resource Language Information Processing
ACM Transactions on Asian and Low-Resource Language Information Processing is a peer-reviewed scholarly journal focusing on computational linguistics, natural language processing, and information processing for Asian and other low-resource languages.
-
B.
NeurIPS
NeurIPS is a premier international conference focused on advances in machine learning, artificial intelligence, and computational neuroscience.
-
C.
NIPS
NIPS is the acronym for the Northern Ireland Prison Service, the government agency responsible for managing prisons and overseeing the custody and rehabilitation of offenders in Northern Ireland.
-
D.
CSLI Publications
CSLI Publications is an academic publishing house associated with Stanford University's Center for the Study of Language and Information, specializing in linguistics, cognitive science, philosophy, and related fields.
-
E.
“A Computer Program for Understanding Natural Language”
“A Computer Program for Understanding Natural Language” is a landmark 1968 paper by Terry Winograd that presents an early natural language understanding system capable of interpreting and executing commands in a simulated blocks world.
- F. None of above. chosen
Statements (59)
| Predicate | Object |
|---|---|
| instanceOf |
computer science conference
ⓘ
natural language processing conference ⓘ scientific conference ⓘ |
| academicDiscipline | computer science ⓘ |
| accepts |
demo papers
ⓘ
industry track papers ⓘ long papers ⓘ short papers ⓘ |
| coLocatedWith |
EMNLP workshops
ⓘ
linked to:
EMNLP
|
| eventType | conference ⓘ |
| field |
computational linguistics
ⓘ
natural language processing ⓘ |
| focus |
data-driven approaches
ⓘ
empirical methods ⓘ machine learning for NLP ⓘ |
| frequency | annual ⓘ |
| fullName | Empirical Methods in Natural Language Processing ⓘ |
| hasAbbreviation | EMNLP ⓘ |
| hasComponent |
demos
ⓘ
industry track ⓘ main conference ⓘ tutorials ⓘ workshops ⓘ |
| hasProceedings | EMNLP proceedings ⓘ |
| hasSeries |
Conference on Empirical Methods in Natural Language Processing
ⓘ
linked to:
EMNLP
|
| isMajorVenueFor |
computational linguistics research
ⓘ
natural language processing research ⓘ |
| language | English ⓘ |
| organizedBy |
ACL Special Interest Group on Linguistic Data and Corpus-Based Approaches to NLP
ⓘ
Association for Computational Linguistics ⓘ |
| organizerAcronym | SIGDAT ⓘ |
| proceedingsAccess | open access ⓘ |
| proceedingsFormat | online ⓘ |
| proceedingsPublisher | Association for Computational Linguistics ⓘ |
| ranking | top-tier NLP conference ⓘ |
| reviewProcess | peer-reviewed ⓘ |
| sponsor | Association for Computational Linguistics ⓘ |
| submissionType |
findings papers
ⓘ
research papers ⓘ short papers ⓘ |
| topic |
datasets and resources for NLP
ⓘ
dialogue systems ⓘ discourse ⓘ ethics in NLP ⓘ evaluation methods in NLP ⓘ fairness and bias in NLP ⓘ information extraction ⓘ interpretability of NLP models ⓘ language modeling ⓘ large language models ⓘ machine translation ⓘ multilingual NLP ⓘ question answering ⓘ semantics ⓘ sentiment analysis ⓘ speech and language processing ⓘ summarization ⓘ syntax and parsing ⓘ text classification ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: EMNLP Description of subject: EMNLP (Empirical Methods in Natural Language Processing) is a leading annual conference in computational linguistics and natural language processing research.
Referenced by (11)
Full triples — surface form annotated when it differs from this entity's canonical label.
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
→
publishedIn
→
EMNLP 2014
ⓘ
linked to: EMNLP
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
→
venue
→
Conference on Empirical Methods in Natural Language Processing
ⓘ
linked to: EMNLP
linked to: EMNLP
linked to: EMNLP
linked to: EMNLP
Association for Computational Linguistics
→
organizesConference
→
EMNLP (Conference on Empirical Methods in Natural Language Processing)
ⓘ
linked to: EMNLP