Semantic Composition
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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.
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.
Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction
COLING 2016
• pgcool/TF-MTRNN
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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 JOINT ENTITY AND RELATION EXTRACTION RELATION CLASSIFICATION SEMANTIC COMPOSITION STRUCTURED PREDICTION
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.
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.
A Survey on Deep Learning for Named Entity Recognition
NER serves as the basis for a variety of natural language applications such as question answering, text summarization, and machine translation.
MACHINE TRANSLATION NAMED ENTITY RECOGNITION QUESTION ANSWERING SEMANTIC COMPOSITION TEXT SUMMARIZATION
No Word is an Island -- A Transformation Weighting Model for Semantic Composition
• sfb833-a3/commix
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Composition models of distributional semantics are used to construct phrase representations from the representations of their words.