It refers to the relationship established by the semantic category of the word hidden behind the syntactic structure, and various detailed semantic relationships are recommended to be viewed against the PPT (in fact, it is what we think is a grammatical problem). At the same time, it has a certain development history, so this part is not described here. This article focuses on explaining relation extraction under machine learning

1.1 Syntactic relations

Positional relationship (relation of positioin)
substitution relationship (relationship of substitutability)
co-occurrence relationship (relation of co-occurrence)

1.1.1 Substitution relationship

1.1.3 Relations of Co-occurence

2 Usefulness of relation extraction

Build knowledge base text analysis NLP application
Information extraction
Information retrieval.
Automatic summary
machine translation
Q&A
Paraphrase
Textual implication reasoning
Thesaurus builds a semantic network
Word Meaning Disambiguation
Language modeling

2.1 Features in relation extraction

2.1.1 Methods of learning semantic relations

Supervised learning

Advantages: very good performance
Disadvantages: Need a lot of labeling data and feature representation

Unsupervised learning

Advantages: Scalable, suitable for open information extraction
Disadvantages: poor performance

2.1.2 Features

Purpose: Map data into entity characteristics and relationship characteristics

2.1.3 Basic entity features

The basic entity features include the string value of each candidate parameter and the individual words that mark these parameters , which may be lemmatization or stemming,
for example:
string value\individual words, morphological words or stemming

Advantages: In most cases, such features are informative enough for a good relationship
Disadvantages: Such features tend to be sparse

Background entity features

Syntactic information, such as: grammatical roles
Semantic information, such as: semantic category

A list of specific tasks may be used, such as: ACE entity types
may use generalized vocabulary resources, such as: WorldNet

Advantages: solve the problem of data sparsity
Disadvantages: requires human resources
Advantages: It is possible to capture its meaning by aggregating a word and interacting with all other words in a large text collection.
Disadvantages: will mix different meanings together

2.1.4 Relationship characteristics

Directly characterize the relationship,
For example, modeling the context of an entity.
Word between two parameters
Word in a specific window or side of the parameter
Dependent path of link parameters
A complete dependency graph
Minimal ruler

Background relational features

Encode knowledge about how entities usually interact, not just context
Relational representation through paraphrase
Placeholder pattern
Find similar contexts through clustering

3 Relation extraction data set

3.1 Annotated data for semantic relationship learning

At last year's EMNLP2018, FewRel released a large-scale fine-labeled relation extraction data set by the Natural Language Processing Laboratory of Tsinghua University led by Professor Maosong Sun. Using Wikipedia as the corpus and Wikidata as the knowledge graph, it contains 100 categories and 70,000 examples, which completely surpasses the previous similarly labeled data sets.
Significance:
Applied to classic supervision/remote supervision relationship extraction tasks, it also has great exploratory value and broad application prospects in emerging few-shot learning tasks

3.2 Entity Relationship Extraction Based on Template

3.2.1 Template-based approach

Use patterns (rules) to mine relationships, based on trigger words/strings, etc.
Dependency-based syntax
Relationship mining model

Hearst's list of patterns

Dependency-based syntax

advantage:

Artificial rules have high accuracy (high-precision)
Can be customized for specific fields (tailor)
Easy to implement on small-scale data sets, simple to construct

Disadvantages:

Low recall rate (low-recall)
The template for a specific field needs to be constructed by experts. It is necessary to consider all possible patterns thoroughly.
Difficult and time-consuming
Need to define the pattern for each relationship
Difficult to maintain
Transplantability difference

4 Extraction of supervised entity relationship

Feature vector-based method --- extract a series of features from contextual information, part of speech, grammar, etc.
Nuclear classification relationship features---may have a complex structure
Sequence labeling method---The span of the parameters in the relationship is variable.

4.1 Method based on feature vector

4.2 Nuclear classification

5 Weakly supervised entity relationship extraction

5.1 Bootstrapping

Semantic drift example

5.2 Label Propagation

method

5.3 Co-learning method

Basic process:

Choose 2 different classifiers,
Use independent features to train on 2 training sets and test on the unlabeled set respectively
Select instances with high confidence to extend to the training set of another classifier
Iterate several times in this way, and stop when the accuracy reaches the threshold.

6 Remote supervision of entity relationship extraction

6.1 Remote supervision

7 Unsupervised entity relationship extraction

8 Relation extraction based on deep learning

Word embedding

What is it? How to
map a word to a real-valued low-dimensional space vector
?

neural networks (e.g., CBOW, skip-gram) [Mikolov&al.2013a]
dimensionality reduction (eg, LSA, LDA, PCA )
explicit representation (words in the context)

Why should we pay attention to it?
Word embedding is important for many NLP tasks, including relation extraction

8.1 Word Embedding-Neural Network NMLM Model

Classic paper: Efficient Estimation of Word Representations in Vector Space

This article is the first one of word2vec, and proposes two big models of CBOW and Skip-gram

8.1.1 CBOW

frame:

What CBOW has to do is to use the words before and after
a word, called its context, to predict it.

8.1.2 Skip-gram model

It seems to be the opposite of CBOW. It accepts a word as input and predicts the words in its context. As mentioned in the article, increasing the context window can improve the quality of the word vector.

Distributed Representations of Words and Phrases and their Compositionality

This article is the second attack of word2vec, and proposes some tips for improving
Skip-gram:

The nature of projection with PCA Skip-gram:
word embedding has a linear structure, which can achieve analogy with vector arithmetic. Due to the training goal, the input and output (before softmax) have a linear relationship

Linguistic Regularities in Continuous Space Word Representations [Mikolov &al.2013]

This article mainly introduces the characteristics of word vectors supporting basic algebraic operations, and uses this feature to apply them to SemEval 2012 Task to measure
the word vectors in this paper are learned through RNNLM.
s(t) = f(Uw(t) + Ws(t-1))

Semantic Compositionality through Recursive Matrix-Vector Spaces

MV-RNN: Matrix-Vector R NN

The model in this article assigns a vector and a matrix to each node in the parse tree:

The vector captures the inner meaning of the ingredients
The matrix captures how it changes the meaning of adjacent words or phrases

Experimental results (using the data set and evaluation of SemEva1-2010 Task 8)

Relation Classification via Convolutional Deep Neural Network [Zeng&al., 2014]

Experimental results

Knowledge Graph Getting Started Study Notes (6)-Relationship Extraction

0 Preface:

1 Semantic relationship: