Anastasios Martidis developed a system to extract maritime safety events from news articles. The system uses rule-based text classification, information extraction, and named entity recognition to identify event types, ships, locations, dates, and relations between extracted information. Evaluation on a test set showed the system achieved over 80% precision and recall for most extraction tasks, though ship name extraction was less accurate. The system accomplishes the research goal but could be improved, particularly for ship name extraction.

1. Vrije Universiteit
MSc Information Sciences
Maritime Safety Events Extraction
from News Articles
Anastasios Martidis
anastasios.martidis@student.vu.nl
July 31, 2012
Supervisors:
Willem R. van Hage, Dr
Davide Ceolin, MSc
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2. Outline
 Introduction  Training Sets
 Information  System Overview
Spectrum  Test sets
 Problem Statement  Evaluation
 Significance of  Results
Research  Conclusions
 Research Questions
 Hypotheses
 Materials and
Methods
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3. Introduction
“We are drowning in information, and
starved for knowledge. ”
John Naisbitt
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4. Information Spectrum
Structured Data: Automatic Identification System (AIS)
theoceandreamer.files.wordpress.com/
2011/03/img_21861.jpg
Free Text: News Articles
http://www.tideway.nl/images/NorthWestEveningMail-
PortSettoRockasTurbinesGetBoostfromaRollingstone-Walney2010-kleinbestan.jpg
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5. Problem Statement
News Articles:
 Descriptive and informative, but…
 Vast in number, daily growing and updated
 Free text, difficult to process automatically
 Generic Natural Language Processing tools:
 Popular and useful, but…
 Present limitations in recognizing specific
types of maritime safety events and ship
names
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6. Significance of the Research
Applications Potential Stakeholders
 Risk assessments  Ship owners, operators
 Improvement of vessel and managers
safety standards  Insurance Companies
 Port facility security  Coast Guard
assessments  International Maritime
 Recognition of problematic Organization (IMO)
areas (Piracy)  International Maritime
 Identification of shipping Security (IMS)
companies, ships, ship  Private Security
constructors with history Companies (PCSs)
in maritime safety events
 Maritime education and
training
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7. Research Questions
1. Can we automatically process a news article in order to
determine if it concerns a maritime safety event?
2. Can we automatically extract a description of a maritime
safety event? The objective of the description is to
automatically recognize the type of maritime safety event,
ships involved, location, date and time.
3. Can we recognize relations and significance of the
extracted information from the text?
-Can we recognize the dominant event? Dominant
event is considered the event that is primarily described
in the news article.
-Can we identify relations between extracted locations
and specific event types described in the text?
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8. Hypotheses
1. We can define sets of keywords that if are present in
certain combinations in the text under processing, indicate
that it concerns a maritime safety event.
2. We can extract a description for the event described in
the news article using rule based text classification and
sets of keywords, datasets of ship names, regular
expressions matching and Name Entity Recognition tasks.
3. We can evaluate the extracted information from the text:
-identifying the dominant event by measuring the
frequency of keyword indicators for each event type
-recognize relation between locations and event types
by examining the position of locations and event type
indicators in the text
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9. Materials & Methods
 Rule Based Text Classification
 Information Extraction
 OpenCalais
 NLTK
 AIS
 dbpedia
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10. Training Set
 200 news articles (retrieved from CBS news)
 100 related to maritime safety (53937 tokens)
 100 of general domains (47053 tokens)
 Word Frequency
Maritime Safety Related General Domains
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11. Training Set Outcomes
 Manual discrimination of significant words
 Categorize into sets of keywords by their
meaning
 Use of keywords for text classification
 Mapping of keywords into maritime safety
event types
 Use of keywords as event type indicators
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12. Text Classification
 Document D
 Lists of keywords:
L1, most frequent keywords
L2, safety related keywords
L3, vessel type keywords
L4, maritime related keywords
L5, naval hierarchy keywords
L6, part of ship keywords
L7, water based locations keywords
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13. Event Type Recognition
 Document D,
 Event Types (ET):
Piracy Capsizing
Sinking Drifting
Oil spill Leakage
Fire/Explosion Evacuation
Grounding Collision
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14. Ship Names Extraction
 Datasetof ship names retrieved from AIS
messages and dbpedia
 Comparison of the dataset entries to the
text
 Compromises
 Location names
 Part of names
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15. Locations Extraction
 Use of OpenCalais for NER tasks
 Interested in locations only
 Four types of locations recognized by
Calais:
Continent
Country
City
Provenance or State
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16. Date and Time Extraction
 Chucked sentences
 Pattern matching using regular
expressions
 Numeric representation of date (e.g., 1322012, 22-07-12)
 Months (e.g., January or Jan.)
 Days (e.g., Monday or Mon.)
 Day periods (e.g., morning, afternoon)
 Time (e.g., 11:00am or 11.00 a.m.)
 Presented in specific order for each
sentence
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17. Dominant Event Recognition
 For each list of event type indicators
keywords
 Sum of keywords occurrence in the text
 Event type with the highest sum is
predicted as the dominant event
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18. Location to Event Relations
 Chunked sentences
 For every sentence containing an
extracted location, if a keyword indicator
of an event type also occurs in the same
sentence
 Then is predicted that the location is
related to the event type
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19. Test Set
 200 news articles (BBC, Reuters)
 100 maritime safety related
 100 of general domains (50 of them
selected as an attempt to mislead the
system)
 Each news article manually labeled and
automatically processed by the system
 Comparison of the results to the labeled
news article
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20. Labeled News Article
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21. Results of the System
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22. Evaluation
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23. Results: Text Classification
Precision: 100 %
Recall: 100 %
F-measure: 100 %
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24. Results: Event Type Recognition
Precision: 88%
Recall: 97 %
F-measure: 92.2 %
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25. Results: Ship Name Extraction
Precision: 18.5%
Recall: 45.3%
F-measure: 26.3%
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26. Results: Location Extraction
Precision: 88.5%
Recall: 74.7%
F-measure: 81%
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27. Results: Date and Time Extraction
Precision: 95.3%
Recall: 89.4%
F-measure: 92.3%
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28. Results: Dominant Event Recognition
Precision: 92%
Recall: 92%
F-measure: 92%
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29. Results: Location to Event Relations
Precision: 81%
Recall: 67.8%
F-measure: 73.8%
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30. Conclusions
 The system accomplished the extraction
of maritime safety events from news
articles
 Overall performance of the system was
satisfying
 The system can be improved and refined
 Ship names extraction require a different
approach
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31. Vrije Universiteit
MSc Information Sciences
Maritime Safety Events Extraction
from News Articles
Anastasios Martidis
anastasios.martidis@student.vu.nl
July 31, 2012
Supervisors:
Willem R. van Hage, Dr
Davide Ceolin, MSc
31