The document discusses the integration of natural language processing (NLP) with graph databases, specifically using Neo4j. It covers various topics such as representing text as graphs, mining word associations, graph-based summarization, keyword extraction, and content recommendation methods. The presentation aims to illustrate how graph models can enhance the understanding and processing of textual data.

1. Natural Language Processing
With Graph Databases
DataDay Texas
January 2016
William Lyon
@lyonwj

2. About
Software Developer @Neo4j
william.lyon@neo4j.com
@lyonwj
lyonwj.com
William Lyon

3. Agenda
• Brief intro to graph databases / Neo4j
• Representing text as a graph
• NLP tasks
• Mining word associations
• Graph based summarization and keyword
extraction
• Content recommendation

4. Agenda
• Brief intro to graph databases / Neo4j
• Representing text as a graph
• NLP tasks
• Mining word associations
• Graph based summarization and keyword
extraction
• Content recommendation
Survey of NLP
methods with graphs

5. Intro to Graph Databases / Neo4j

6. Charts

7. Charts Graphs

8. Neo4j
Graph Database
• Property graph data model
• Nodes and relationships
• Native graph processing
• Cypher query language

9. The Whiteboard Model Is the Physical Model

10. Relational Versus Graph Models
Relational Model Graph Model
KNOWS
KNOWS
KNOWS
ANDREAS
TOBIAS
MICA
DELIA
Person FriendPerson-Friend
ANDREAS
DELIA
TOBIAS
MICA

11. Property Graph Model Components
Nodes
• The objects in the graph
• Can have name-value properties
• Can be labeled
Relationships
• Relate nodes by type and
direction
• Can have name-value properties
CAR
DRIVES
name: “Dan”
born: May 29, 1970
twitter: “@dan”
name: “Ann”
born: Dec 5, 1975
since:
Jan 10, 2011
brand: “Volvo”
model: “V70”
LOVES
LOVES
LIVES WITH
OW
NS
PERSON PERSON

12. Cypher: Graph Query Language
CREATE (:Person { name:“Dan”} ) -[:LOVES]-> (:Person { name:“Ann”} )
LOVES
Dan Ann
LABEL PROPERTY
NODE NODE
LABEL PROPERTY

13. “So what does this have to do with NLP?”
“Am I in the wrong talk?”
“I thought this was going to be about text processing….”

14. Natural Language Processing With Graphs

15. Natural Language Processing With Graphs
Uncovering meaning from text using a graph data model.

16. Representing Text As A Graph
“Nearly all text processing starts
by transforming text into vectors.”
- Matt Biddulph
www.hackdiary.com

17. Representing text as a graph
Text Adjacency Graph

18. Representing text as a graph
Text Adjacency Graph

19. My cat eats fish on Saturday.

22. Convert to array of words

23. Iterate with counter variable i,
from 0 to number of words - 2

24. Get or create node for
words at index i and i+1

25. Create :NEXT relationship

26. Representing A Text Corpus As A Graph

28. Add followship frequency

29. Add word counts

30. Query Word frequency

31. Query Word pair frequencies (colocation)

32. NLP Tasks

33. Mining Word Associations

34. Word Associations
• Paradigmatic
• words that can be substituted
• “Monday” <—> “Thursday”
• “cat” <—> “dog”
• Syntagmatic
• words that can be combined with each other
• “cold”, “weather”
• colocations

35. Computing Paradigmatic Similarity
1. Represent each word by its context
2. Compute context similarity
3. Words with high context similarity likely have
paradigmatic relation

36. Paradigmatic Similarity
1. Represent each word by its context

37. Paradigmatic Similarity
1. Represent each word by its context

38. Paradigmatic Similarity
1. Represent each word by its context
Left1 Right1

39. Paradigmatic Similarity
2. Compute context similarity

40. Paradigmatic Similarity
2. Compute context similarity

41. Paradigmatic Similarity
2. Compute context similarity
www.lyonwj.com/2015/06/16/nlp-with-neo4j/

42. Paradigmatic Similarity
3. Find words with high context similarity
http://earthlab.uoi.gr/theste/index.php/theste/article/viewFile/55/37CEEAUS corpus

43. Paradigmatic Similarity
Example
http://www.lyonwj.com/2015/06/16/nlp-with-neo4j/
https://github.com/johnymontana/nlp-graph-notebooks
https://class.coursera.org/textanalytics-001

44. Graph Based Summarization
and Keyword Extraction

46. image credit: https://en.wikipedia.org/wiki/PageRank
https://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf
https://github.com/summanlp/textrank
Keyword Extraction

47. Summarization
Opinion mining

48. • Opinion mining
• Summarize major opinions
• Concise and readable
• Major complaints /
compliments

49. http://kavita-ganesan.com/opinosis
1.Graph based representation of
review corpus
2.Find and score candidate
summaries
3.Select top scoring candidates
as summary

50. Opinion Mining - Example
• Best Buy API
• Product reviews by SKU

51. Opinion Mining - Example

52. Opinion Mining - Example

53. Opinion Mining - Example
1.Graph based representation
of review corpus
2.Find and score candidate
summaries
3.Select top scoring candidates
as summary

54. Opinion Mining - Example
Find highest ranked paths of 2-5 words

55. Opinion Mining - Demo
“Easy to read in sunlight”
“Comfortable great sound quality”
“I love this washer”

56. Opinion Mining - Demo
“Bought this smart TV for the price”
“Easy to use this vacuum”

57. Opinion Mining - Demo
• iPython notebook
https://github.com/johnymontana/nlp-graph-notebooks

58. Content Recommendation

59. Content recommendation
“Networks give structure to the conversation
while content mining gives meaning.”
http://breakthroughanalysis.com/2015/10/08/ltapreriitsouda/
- Preriit Souda

60. Using Data Relationships for
Recommendations
Content-based filtering
Recommend items based on what
users have liked in the past
Collaborative filtering
Predict what users like based on the
similarity of their behaviors,
activities and preferences to others
Movie
Person
Person
RATED
SIMILARITY
rating: 7
value: .92

61. Using Data Relationships for
Recommendations
Content-based filtering
Recommend items based on what
users have liked in the past
Movie
Person
Person
RATED
SIMILARITY
rating: 7
value: .92

62. The article graph - data model

63. Building the article graph
• Articles users have shared
• Extract keywords using newspaper3k
python library
• Insert in the graph
• Scrape additional articles
https://github.com/johnymontana/nlp-graph-notebooks

64. The article graph - example

65. What are the keywords of the articles I liked?

67. Summary
• Property graph model
• Represent text as a graph
• Word associations
• Opinion mining
• Content recommendation

68. Resources

69. graphdatabases.com

70. Resources
• http://kavita-ganesan.com/opinosis
• http://jexp.de/blog/2015/01/natural-language-
analytics-made-simple-and-visual-with-neo4j/
• https://github.com/johnymontana/nlp-graph-notebooks

71. Opinion Mining
• “Opinosis: A Graph Based Approach to Abstractive
Summarization of Highly Redundant Opinions”
• - Kavita Ganesan, Cheng Xiang Zhai, Jiawei Han University
of Illinois at Urbana-Champaign
• Multi-sentence compression: Finding shortest paths in word
graphs
• - Proceedings of the 23rd International Conference on
Computational Linguistics. COLING 10. Beijing, Cina
Aug23-27, 2010. Katy Fillipova