Supervised Sequence Labelling with Recurrent Neural Networks
E736828
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.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Supervised Sequence Labelling with Recurrent Neural Networks canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T8482877 — 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: Supervised Sequence Labelling with Recurrent Neural Networks Context triple: [Alex Graves, authorOf, Supervised Sequence Labelling with Recurrent Neural Networks]
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A.
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.
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B.
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
"Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation" is a seminal research paper that introduced the RNN encoder–decoder architecture to learn continuous phrase representations for improving statistical machine translation quality.
-
C.
Distributed Representations of Sentences and Documents
"Distributed Representations of Sentences and Documents" is a seminal machine learning paper that introduced the Paragraph Vector (Doc2Vec) method for learning continuous vector representations of variable-length text such as sentences, paragraphs, and documents.
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D.
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
"Long-term Recurrent Convolutional Networks for Visual Recognition and Description" is a research paper that introduces a deep learning architecture combining convolutional and recurrent neural networks to perform tasks like video recognition and automatic image or video captioning.
-
E.
Pointer Networks
Pointer Networks are a type of neural network architecture that uses attention mechanisms to output discrete positions in an input sequence, enabling solutions to combinatorial problems like sorting and the traveling salesman problem.
- 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: Supervised Sequence Labelling with Recurrent Neural Networks Target entity description: 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.
-
A.
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.
-
B.
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
"Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation" is a seminal research paper that introduced the RNN encoder–decoder architecture to learn continuous phrase representations for improving statistical machine translation quality.
-
C.
Distributed Representations of Sentences and Documents
"Distributed Representations of Sentences and Documents" is a seminal machine learning paper that introduced the Paragraph Vector (Doc2Vec) method for learning continuous vector representations of variable-length text such as sentences, paragraphs, and documents.
-
D.
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
"Long-term Recurrent Convolutional Networks for Visual Recognition and Description" is a research paper that introduces a deep learning architecture combining convolutional and recurrent neural networks to perform tasks like video recognition and automatic image or video captioning.
-
E.
Pointer Networks
Pointer Networks are a type of neural network architecture that uses attention mechanisms to output discrete positions in an input sequence, enabling solutions to combinatorial problems like sorting and the traveling salesman problem.
- F. None of above. chosen
Statements (44)
| Predicate | Object |
|---|---|
| instanceOf |
book
ⓘ
scientific monograph ⓘ |
| aimsTo | provide a systematic treatment of RNNs for sequence labelling ⓘ |
| author | Alex Graves ⓘ |
| context | neural network approaches to sequence processing ⓘ |
| covers |
handwriting recognition
ⓘ
labeling unsegmented sequence data ⓘ offline handwriting recognition ⓘ online handwriting recognition ⓘ phoneme recognition ⓘ speech recognition ⓘ |
| emphasizes |
handling variable-length input sequences
ⓘ
learning from unsegmented sequence data ⓘ |
| explains |
LSTM architecture
ⓘ
bidirectional LSTM ⓘ decoding methods for sequence labelling ⓘ recurrent neural network architectures ⓘ regularization for RNNs ⓘ training algorithms for RNNs ⓘ |
| field |
artificial intelligence
ⓘ
handwriting recognition ⓘ machine learning ⓘ pattern recognition ⓘ speech recognition ⓘ |
| focusesOn |
end-to-end training on unsegmented data
ⓘ
supervised sequence learning ⓘ |
| introduces | connectionist temporal classification loss ⓘ |
| isConsidered | foundational work on RNN-based sequence labelling ⓘ |
| language | English ⓘ |
| mainTopic |
backpropagation through time
ⓘ
bidirectional recurrent neural networks ⓘ connectionist temporal classification ⓘ gradient-based training ⓘ long short-term memory ⓘ recurrent neural networks ⓘ sequence labelling ⓘ sequence modelling ⓘ supervised learning ⓘ |
| provides |
experimental results on sequence labelling tasks
ⓘ
theoretical analysis of RNN training ⓘ |
| targetAudience |
graduate students in computer science
ⓘ
researchers in machine learning ⓘ |
| usedIn |
academic research
ⓘ
graduate-level teaching on neural networks ⓘ |
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: Supervised Sequence Labelling with Recurrent Neural Networks Description of subject: 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.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.