Semantic Composition Edit Task
Natural Language Processing
About
Understanding the meaning of text by composing the meanings of the individual words in the text (Source: https://arxiv.org/pdf/1405.7908.pdf)
Benchmarks
Datasets
Greatest papers with code
Modeling Relation Paths for Representation Learning of Knowledge Bases
Representation learning of knowledge bases (KBs) aims to embed both entities and relations into a low-dimensional space.
KNOWLEDGE BASE COMPLETION RELATION EXTRACTION REPRESENTATION LEARNING SEMANTIC COMPOSITION
The Lifted Matrix-Space Model for Semantic Composition
Tree-structured neural network architectures for sentence encoding draw inspiration from the approach to semantic composition generally seen in formal linguistics, and have shown empirical improvements over comparable sequence models by doing so.
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data
As the main discriminative information of a fine-grained image usually resides in subtle regions, methods along this line are prone to heavy label noise in fine-grained recognition.
Ranked #7 on
Fine-Grained Image Classification
on CUB-200-2011
Semantic Compositional Networks for Visual Captioning
The degree to which each member of the ensemble is used to generate an image caption is tied to the image-dependent probability of the corresponding tag.
SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics
ACL 2020
• WadeYin9712/SentiBERT
• 
We propose SentiBERT, a variant of BERT that effectively captures compositional sentiment semantics.
EMOTION CLASSIFICATION SEMANTIC COMPOSITION SENTIMENT ANALYSIS
From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations
This paper presents the first model for time normalization trained on the SCATE corpus.
Ranked #1 on
Timex normalization
on PNT
A Semantically Compositional Annotation Scheme for Time Normalization
We present a new annotation scheme for normalizing time expressions, such as {``}three days ago{''}, to computer-readable forms, such as 2016-03-07.
Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction
COLING 2016
• pgcool/TF-MTRNN
• 
This paper proposes a novel context-aware joint entity and word-level relation extraction approach through semantic composition of words, introducing a Table Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the entity recognition and relation classification tasks to a table-filling problem and models their interdependencies.
ENTITY EXTRACTION USING GAN JOINT ENTITY AND RELATION EXTRACTION RELATION CLASSIFICATION SEMANTIC COMPOSITION STRUCTURED PREDICTION
Improving Semantic Composition with Offset Inference
Count-based distributional semantic models suffer from sparsity due to unobserved but plausible co-occurrences in any text collection.