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Everything about Sentence embedding
Representation in natural language processing
In natural language processing, a sentence embedding is a representation of a natural language text as a vector of numbers which encodes meaningful semantic information. The name stems from the initial limitations of the approach to embed sequences of text longer than a sentence, which are no longer a factor. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Sentence embeddingA 5-minute orientation
- Skim a recent paper: Electrical Fault Diagnosis From Text Data: A Supervised Sentence Embedding Combined With Imbalanced ClassificationInfluential work since 2022
- Pick up a classic book: Born to talkThe most-reprinted book on the subject
- Read the landmark paper: DeepSyslog: Deep Anomaly Detection on Syslog Using Sentence Embedding and MetadataCited 60 times
- Go sideways: BERT (language model)A closely related topic
- Go sideways: Word embeddingA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
DeepSyslog: Deep Anomaly Detection on Syslog Using Sentence Embedding and Metadata
CoSENT: Consistent Sentence Embedding via Similarity Ranking
Rethinking of BERT sentence embedding for text classification
CLSEP: Contrastive learning of sentence embedding with prompt
Sentence Embedding Models for Similarity Detection of Software Requirements
Fast and scalable neural embedding models for biomedical sentence classification
Neural sentence embedding using only in-domain sentences for out-of-domain sentence detection in dialog systems
Modality-Invariant Image-Text Embedding for Image-Sentence Matching
🆕 Influential since 2022
Electrical Fault Diagnosis From Text Data: A Supervised Sentence Embedding Combined With Imbalanced Classification
ColBERT: Using BERT sentence embedding in parallel neural networks for computational humor
Novelty Evaluation using Sentence Embedding Models in Open-ended Cocreative Problem-solving
A Comparative Analysis of Sentence Embedding Techniques for Document Ranking
CLSESSP: Contrastive learning of sentence embedding with strong semantic prototypes
📚 Books worth reading
Born to talkLloyd M. Hulit, Merle R. Howard · 1992
Technical CommunicationJohn M. Lannon · 2000- 📕Working Memory in Sentence ComprehensionShravan Vasishth · 2003
- 📕Explosive magazineCatherine Wagner, Katy Lederer · 1997
The Conspirators HierarchyJohn Coleman · 1992
Treaty Occupationay ToxicologySuelen Queiroz · 2010- 📕Two-Dimensional SemanticsTatjana Scheffler · 2013
Grammar studyJanet Angelillo · 2008
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- BERT (language model) Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. It learns to represent…
- Word embedding In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is…
- Paraphrasing (computational linguistics) Paraphrase or paraphrasing in computational linguistics is the natural language processing task of detecting and generating paraphrases. Applications of…
- Semantic parsing Semantic parsing is the task of converting a natural language utterance to a logical form: a machine-understandable representation of its meaning. Semantic…
- ELMo ELMo (embeddings from language model) is a word embedding method for representing a sequence of words as a corresponding sequence of vectors. It was created…
- Transformer (deep learning) In deep learning, the transformer is a family of artificial neural network architectures based on the multi-head attention mechanism, in which input data…
- Word2Vec In natural language processing, Word2Vec is a technique for obtaining vector representations of words as word embeddings. These vectors capture information…
- Lexical chain A lexical chain is a sequence between two semantically related ordered words in writing. It can span narrow (adjacent words or sentences) or wide context…