It learns compositional representations for sentences recursively.

Recursive Neural Networks

It can better handle long sequential data.

Recurrent Neural Networks (RNNs)

GNNs are a general framework for defining deep neural networks on graph data. The goal of GNNs is to generate representations of nodes that actually depend on the structure of the graph, as well as any feature information available.

Graph Neural Networks (GNNs)

It utilizes attention mechanism to aggregate global relational information.

Attention-based Neural Networks

Different from SRE models, NRE mainly utilizes word embeddings and position embeddings instead of hand-craft features as inputs. Word embeddings are the most used input representations in NLP, which encode the semantic meaning of
words into vectors.

Word Embeddings

In order to capture the entity information in text, position embeddings are introduced to specify the relative distances between words and entities.

Position Embeddings

Convolutional networks, also known as convolutional neural networks, or CNNs, are a specialized kind of neural network for processing data that has a known grid-like topology. Examples include time-series data, which can be thought of as a 1-D grid taking samples at regular time intervals, and image data, which can be thought of as a 2-D grid of pixels. 

The network employs a mathematical operation called convolution. Convolution is a specialized kind of linear operation. Convolutional networks are simply neural networks that use convolution in place of general matrix multiplication in at least one of their layers.

The CNN gets its power from the following concepts:
- Sparse connectivity
- Parameter sharing

Convolutional Neural Network (CNN)

Neural relation extraction (NRE) models introduce neural networks to automatically extract semantic features from text. Compared with SRE models,
NRE methods can effectively capture textual information and generalize to wider range of data.

New York University

University of Michigan - Ann Arbor

Hamilton College

Relation Extraction Models

Neural relation extraction discovers semantic relations between entities from unstructured text using deep learning methods.

Neural Relation Extraction

Han, X. (n.d.). More Data, More Relations, More Context and More Openness:
A Review and Outlook for Relation Extraction *, Retrieved June 4, 2022, from https://arxiv.org/pdf/2004.03186.pdf

More Relations, More Context and More Openness:
A Review and Outlook for Relation Extraction

The pioneering methods use sentence analysis tools to identify syntactic elements in text, then automatically construct pattern rules from these elements. In order to extract patterns with better coverage and accuracy, later work involves larger corpora, more formats of patterns, and more efficient ways of extraction.

Pattern Extraction Models

Statistical methods bring better coverage and require less human
efforts. Thus statistical relation extraction (SRE) has been extensively studied.

Statistical Relation Extraction Models

Neural relation extraction: a survey

Neural relation extraction methods can be categorized regarding their assumptions on expressiveness of training instances about the relations:
- Sentence-level relation extraction
- Bag-level relation extraction
- Document-level relation extraction

Neural Relation Extraction Methods

Supervised neural relation extraction from text uses sentence-level relation extraction approach which requires labeled relation-specific training data. Many of the studies on this task rely on classifying entity pairs according to the particular relations they are assigned to.

Supervised Relation Extraction

Distant supervision aligns triples in a related knowledge graph with the sentences in input text, in order to automatically generate training data. Distant supervision assumes the responsibility to determine which sentence supports which relation and to what degree it expresses the relation of interest.

 Relation Extraction using Distant Supervision

Few-shot learning is a learning method, in which in contrast to regular deep learning methods the amount of available training data is small. The assumption is that reliable algorithms can be built to achieve competitive performance to the models trained with abundant data.

Relation Extraction using Few-Shot Approach

Challenges in neural relation extraction usually regard available data and existing contextual and structural approaches:
- Overlapping triples
- Noise in distant supervision
-  Few-shot instances

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