The document summarizes a presentation on hierarchical entity extraction and ranking using unsupervised graph convolutions. It outlines the proposed approaches which include using constituency parsing to extract mention candidates, BERT to obtain contextualized word embeddings, and graph convolution on a dependency parsing graph and coreference graph to normalize mention embeddings. It describes the experiments on a political news dataset, comparing the proposed graph-based model to baselines. The analysis shows the graph model achieves meaningful entity extraction, ranking and hierarchies.

1. 1
Hierarchical Entity Extraction and Ranking with
Unsupervised Graph Convolutions
Presenter: Zhexiong Liu
Advisor: Jinho D. Choi

2. 2
Outline
• Introduction
• Approaches
• Experiments
• Analysis
• Conclusion
• Reference

3. 3
Outline
• Introduction
• Approaches
• Experiments
• Analysis
• Conclusion
• Reference

4. 4
Informa(on Extrac(on aims to extract structured data
from unstructured data
• Named En((es:
Persons, Organiza.ons, Loca.ons, etc
• A6ributes:
Name, Descriptors, Categories, etc
• Rela(onships:
Person works for Organiza.on
• Events:
Kansas Chiefs won Super Bowl 2020
Background

5. 5
• Keyword Extraction
– Generates a list of phrases from texts
– Without coreference
– Without importance
Related Works

6. 6
• Named Entity Extraction
– Finding named entities in a text
– Conduct classifications
– Without coreference
– Without importance
Related Works

7. 7
• Entity Salience Detection
– Identify main entities
– Binary categories
– Without ranked order
– Without coreference
Related Works

8. 8
• This thesis focus
– Entities Extraction
– Entity Ranking
“Three Miami Dolphins players were spotted taking a
knee on the sideline during the singing of the national
anthem: Kenny Stills, Michael Thomas and Julius
Thomas.”
Proposed Task

9. 9
“Three Miami Dolphins players were spotted taking a
knee on the sideline during the singing of the national
anthem: Kenny Stills, Michael Thomas and Julius
Thomas.”
1. the national anthem
2. a knee
3. Three Miami Dolphins players
Proposed Task

10. 10
Outline
• Introduction
• Approaches
• Experiments
• Analysis
• Conclusion
• Reference

11. 11
Input:
• Given a document D with sequence
Outputs:
• Extracted en((es
• En(ty rank scores
• En(ty hierarchy
Task Formulation

12. 12
• Investigate unsupervised approaches for entity
extraction and ranking
• Determine the effectiveness of contextualized
embeddings for mention representation
• Exam coreference-based and parsing-based graph
convolution for embedding normalization
Research Questions

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• Baseline model
– Constituency parsing
– Mention embedding
Proposed Approaches

14. 14
• The purpose of constituency parsing is to
obtain mention candidates
• Process plaint data without labels
• Develop noun phrase trunker based on
constituency parsing tree
Cons=tuency parsing

15. 15
Constituency parsing
“Kansas City Chiefs won their first Super Bowl on
February 2020”

16. 16
• Rooted trees representing the syntactic
structures
• Trunked phrases may contain multiple or
single tokens (words)
“Kansas City Chiefs won their first Super Bowl on
February 2020”
Constituency parsing

17. 17
• Map word into vectors
• Contextualized world embedding
Word Embedding

18. 18
BERT Word Embedding
http://jalammar.github.io/illustrated-bert/

19. 19
http://jalammar.github.io/illustrated-bert/
BERT Word Embedding

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• Sum last four layers of BERT to obtain
contextualized word embedding
• Average each word embeddings in a mention
to present phrase embeddings
• Map phrases (mentions) into vectors
Phrase Embedding

21. 21
• A graph for clustering
• Nodes are menIon set
• Each node has a phrase embedding
• Edges distance between and
Construct Cluster Graph

22. 22
• The density of adjacency matrix for cluster
graph
Perform Clustering

23. 23
HDBSCAN Clustering
• No need to indicate the number of clusters
• Cluster with noises, which could be singletons
• Perform hierarchy for cluster results
Minimum
Spanning
Tree
Single-
linkage
Hierarchy
Cluster
Graph
Cluster
results

24. 24
Entity Ranking
• Based on the mention hierarchy, each
mention has a score
• Each clusters or singletons maintain a score

25. 25
• Heavily rely on contextualized embeddings
• Cannot handle syntactics in the sentences
• May extract duplicate entities
• Cluster graph is not quite sparse-dense
oriented to perform HDBSCAN
Baseline Drawbacks

26. 26
• Coreference model
– En(ty coreference
– Men(on normaliza(on
Proposed Approaches

27. 27
• Link the entity mentions that refer to the
same entity across document.
Coreference Resolution

28. 28
• Based on BERT model
• Mask spans instead of tokens
SpanBERT Coreference

29. 29
Problem: Two mentions have different spans
• Mention cluster from SpanBERT:
– {Kansas City Chiefs, This team, their, the Chiefs}
• Mention extracted from Constituency Parsing:
– {Kansas City Chiefs, their first Super Bowl victory,
the chiefs' first NFL champion}
Men=on Alignments

30. 30
Alignment rule: Half-Half
• If two mentions have half overlap, the parser-
based one is assigned to an entity cluster.
• Convert coreference-based mention cluster to
parser-based mention cluster
Mention Alignments

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• Normalize mention embedding
• Mentions outside coreference clusters
• Mentions inside coreference clusters
Coreference Model

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• Construct cluster graph
• Each node has normalized embedding
based on coreference resolution cluster it it
fall into a cluster, otherwise,
• Edges distance
• Perform HDBSCAN
• Obtain Entity ranking and hierarchy
Coreference Model

33. 33
• Alignment problems
– “their first Super Bowl victory” vs “their”
• Coreference introduce noises
– “the chiefs’” vs “the chiefs’ first championship”
• Missing syntacIc informaIon within sentence
Coreference Model Drawbacks

34. 34
• Graph-convolution model
– Perform graph convolution
– Dependency parsing
Proposed Model

35. 35
Graph-convolution model
• Dependency parsing graph that considers the
mention relation within sentence
• Coreference graph that considers the mention
relation between sentences

36. 36
• Structures capturing implicit dependencies
between tokens in a sentence
Dependency Parsing

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• Construct a dependence-graph and a
coreference graph
• Nodes are entity mentions with embedding
• Edges are the inner-sentence or inter-
sentence distance
Graph-convolution Model

38. 38
• Distance on dependency graph defines as the
number of edges on the shortest path that connects
two head nouns of the mentions
Graph-convolution Model

39. 39
• The distance on coreference graph defines as the size
of coreference cluster
Graph-convolution Model

40. 40
• Given adjacency matrix A, degree matrix D,
IdenIty matrix I, and feature (menIon
embedding) matrix: X
• Normalized Laplacian Matrix
• A filter for dependency graph
Graph-convolution Model

41. 41
• Graph convolution aims to update node
embeddings
• K-order convolution leverages K-order
neighbors of current nodes
• Updated embedding feature matrix is
Graph-convolution Model

42. 42
• Combine the dependency graph mention
embedding and coreference graph
mention embedding
Graph-convolution Model

43. 43
• Word embedding based on BERT
• Mention embedding via averaging tokens
• Coreference embedding based on SpanBERT
• Normalized embedding based on dependency
graph convolution and coreference graph
convolution
Graph-convolu=on Model

44. 44
• Political news dataset NELA2017 with news
between April 2017 and October 2017.
• Sample of 60 articles
• Annotated entity by three annotators
• Each annotator annotated all the entity in the
text and marked top 3, top 5 and top 10
Data Annotation

45. 45
Outline
• Introduction
• Approaches
• Experiments
• Analysis
• Conclusion
• Reference

46. 46
Data Annotation

47. 47
• EnIty staIsIcs in annotated arIcles
• Total enIty annotated vs enIty length
Data Exploration

48. 48
• Perform baseline, coreference model, and
graph-based model on 60 articles
Experimental Results

49. 49
Outline
• Introduction
• Approaches
• Experiments
• Analysis
• Conclusion
• Reference

50. 50
WASHINGTON NFL Sunday kicked off in London with more kneeling during the
national anthem, as President Trump continues to admonish players who don’t stand
for the flag.
Three Miami Dolphins players were spotted taking a knee on the sideline during
the singing of the national anthem: Kenny Stills, Michael Thomas and Julius Thomas.
The three kneeled side-by-side at one of the sidelines while the rest of their team stood
for the anthem.
Meanwhile, their opposing team, the New Orleans Saints, took a unified knee on
the sidelines but rose in time for the national anthem. Many of Saints linked arms as
Grammy-winning artist Darius Rucker sang the anthem in Wembley Stadium, the first
televised game of the day on Fox. The cameras didn’t zoom in on the Dolphins
kneelers, but attendees and journalists in London quickly posted pictures of the trio of
kneelers on social media.
President Trump has blasted a kneeling player as a son of a bitch and urged
owners to fire athletes who don’t stand up for the flag.
Ar=cle Example

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• Right: embedding based on BERT
• left: embedding based on BERT & Coreference
Entity Embedding Comparison

52. 52
• baseline, coreference model, graph-based model
with coreference embedding, and graph-based
model without coreference embedding
Sparse and Density

53. 53
MST on Cluster Graph

54. 54
• Top 10 enIty (clusters) and their ranking
Entity Extraction & Ranking

55. 55
• Meaningful hierarchy with importance
Entity Hierarchy

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Outline
• Introduction
• Approaches
• Experiments
• Analysis
• Conclusion
• Reference

57. 57
• Unsupervised approaches for entity extraction and
ranking demonstrate promising results
• Contextualized word & phrase embeddings show
considerable semantics
• Coreference-based and parsing-based graph convolution
for embedding normalization archive good results
• Constituency parsing and dependency parsing performs
significant syntactics
Conclusion

58. 58
• Proposed a novel task with entity extraction, entity ranking,
and entity hierarchy
• Perform unsupervised learning without training
• Collaboratively annotated 60 articles based on NELA2017
• Demonstrated a simple yet effective normalization
mechanism with coreference resolution
• Employ entity relation graph for graph convolution
Contribution

59. 59
• Construct more meaningful graph that could be used
to update the node embeddings
• Fine-tune SpanBERT on NELA2017 to improve
mention representation
• Leverage title and position features to improve entity
ranking
• Apply entity extraction and ranking to event
Further Works

60. 60
Outline
• Introduction
• Approaches
• Experiments
• Analysis
• Conclusion
• Reference

61. 61
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