Extensions of recurrent neural network language model
E906312
"Extensions of Recurrent Neural Network Language Model" is a research work by Tomas Mikolov that advances neural language modeling by improving and extending recurrent neural network architectures for better performance in natural language processing tasks.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Extensions of recurrent neural network language model canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T11108942 — 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: Extensions of recurrent neural network language model Context triple: [Tomas Mikolov, notableWork, Extensions of recurrent neural network language model]
-
A.
Exploring the Limits of Language Modeling
"Exploring the Limits of Language Modeling" is a research paper that investigates how far large-scale neural language models can be pushed in terms of performance, scalability, and generalization on natural language tasks.
-
B.
Sequence to Sequence Learning with Neural Networks
"Sequence to Sequence Learning with Neural Networks" is a seminal 2014 paper that introduced the sequence-to-sequence (seq2seq) neural network framework for tasks like machine translation, laying the groundwork for many modern NLP models.
-
C.
Generating sequences with recurrent neural networks
"Generating Sequences with Recurrent Neural Networks" is a highly influential research paper by Alex Graves that advanced the use of RNNs for tasks like handwriting and text generation by demonstrating powerful sequence modeling and generation capabilities.
-
D.
Sequence transduction with recurrent neural networks
"Sequence transduction with recurrent neural networks" is a seminal research paper by Alex Graves that introduced powerful RNN-based methods for mapping input sequences to output sequences, influencing modern sequence-to-sequence and attention models in machine learning.
-
E.
Supervised Sequence Labelling with Recurrent Neural Networks
Supervised Sequence Labelling with Recurrent Neural Networks is a foundational monograph that systematically presents the theory, architectures, and training methods for applying recurrent neural networks to tasks such as speech recognition, handwriting recognition, and other sequence labeling problems.
- 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: Extensions of recurrent neural network language model Target entity description: "Extensions of Recurrent Neural Network Language Model" is a research work by Tomas Mikolov that advances neural language modeling by improving and extending recurrent neural network architectures for better performance in natural language processing tasks.
-
A.
Exploring the Limits of Language Modeling
"Exploring the Limits of Language Modeling" is a research paper that investigates how far large-scale neural language models can be pushed in terms of performance, scalability, and generalization on natural language tasks.
-
B.
Sequence to Sequence Learning with Neural Networks
"Sequence to Sequence Learning with Neural Networks" is a seminal 2014 paper that introduced the sequence-to-sequence (seq2seq) neural network framework for tasks like machine translation, laying the groundwork for many modern NLP models.
-
C.
Generating sequences with recurrent neural networks
"Generating Sequences with Recurrent Neural Networks" is a highly influential research paper by Alex Graves that advanced the use of RNNs for tasks like handwriting and text generation by demonstrating powerful sequence modeling and generation capabilities.
-
D.
Sequence transduction with recurrent neural networks
"Sequence transduction with recurrent neural networks" is a seminal research paper by Alex Graves that introduced powerful RNN-based methods for mapping input sequences to output sequences, influencing modern sequence-to-sequence and attention models in machine learning.
-
E.
Supervised Sequence Labelling with Recurrent Neural Networks
Supervised Sequence Labelling with Recurrent Neural Networks is a foundational monograph that systematically presents the theory, architectures, and training methods for applying recurrent neural networks to tasks such as speech recognition, handwriting recognition, and other sequence labeling problems.
- F. None of above. chosen
Statements (38)
| Predicate | Object |
|---|---|
| instanceOf |
natural language processing paper
ⓘ
research paper ⓘ scientific publication ⓘ |
| affiliationOfAuthor | Tomas Mikolov NERFINISHED ⓘ |
| aimsTo |
enhance performance on NLP tasks
ⓘ
improve generalization of language models ⓘ reduce perplexity on language modeling benchmarks ⓘ |
| author | Tomas Mikolov NERFINISHED ⓘ |
| contributesTo |
advances in neural language modeling
ⓘ
improved performance of language models ⓘ |
| field |
computational linguistics
ⓘ
machine learning ⓘ natural language processing ⓘ |
| focusesOn |
better performance on natural language processing tasks
ⓘ
extensions of recurrent neural network language models ⓘ improving recurrent neural network architectures for language modeling ⓘ |
| hasAuthor | Tomas Mikolov NERFINISHED ⓘ |
| hasCitationType | computer science paper ⓘ |
| hasKeyword |
RNN
NERFINISHED
ⓘ
language modeling ⓘ machine learning ⓘ natural language processing ⓘ neural language model ⓘ recurrent neural network language model ⓘ |
| hasMainConcept |
language model
ⓘ
recurrent neural network ⓘ sequence modeling ⓘ |
| language | English ⓘ |
| proposes | extensions to standard recurrent neural network language models ⓘ |
| relatedTo |
Recurrent Neural Network based Language Model
ⓘ
deep learning for NLP ⓘ neural probabilistic language model ⓘ statistical language modeling ⓘ |
| researchArea |
language modeling
ⓘ
neural language models ⓘ recurrent neural networks ⓘ |
| usesMethod |
neural network language modeling
ⓘ
recurrent neural network ⓘ |
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: Extensions of recurrent neural network language model Description of subject: "Extensions of Recurrent Neural Network Language Model" is a research work by Tomas Mikolov that advances neural language modeling by improving and extending recurrent neural network architectures for better performance in natural language processing tasks.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.