Voxxed Athens 2018 - A scalable maritime platform providing services through an NLP-based intelligent personal assistant by Nikos Gkanatsios

1. A Scalable Maritime Platform Providing
Services
Through An NLP-based Intelligent
Personal Assistant
Voxxed Days Athens 2018
NIKOS GKANATSIOS
AI Engineer

2. METIS CYBERSPACE TECHNOLOGY SA
METIS CYBERSPACE TECHNOLOGY SA is a company member of Olympia Group.
Our VISION
is to set at the disposal of maritime transportation our high expertise and innovative thinking in
the fields of Information Intelligence in order to strengthen the safety of maritime routes, to
minimize damage to the environment and to reduce the sea transport cost of products and people.
Our MISSION
is the development of products that will apply the technologies of the 4th Industrial Revolution in
Maritime Transportations, playing a leading role in the inevitable evolution of Maritime sector that
induces rapid technological progress in all aspects of human activity.
N. Gkanatsios - Voxxed Days Athens 2018 © METIS CYBERSPACE TECHNOLOGY 2

3. Is Digital Shipping a solution ?
Or
Just another incrementation factor of the number and the diversity of IT tools that the operators have to use daily?
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4. Hello METIS!
The first
Cyber-Assistant
dedicated to
Ship Management
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5. METIS Concept
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6. Metis SPACE
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7. AI that talks to you !
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8. NLP Implementation
In depth
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9. NLP Pipeline
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10. Used Technologies
• Bot integration with the various communication channels using
Microsoft Bot Framework.
• The main dialog flow is developed in Node JS.
• The Natural Language Processing pipeline is developed in Python.
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11. Step1 –
Message
Simplification
• Spelling Correction, using
PyEnchant, Named Entity
Recognition and Norvig's One-
Edit-Away Algorithm.
• Abbreviations Handling, using a
list of the most common maritime
abbreviations.
• Conjunctions' Splitting, based on
Dependency Trees and SpaCy.
The dependency tree for the complex sentence "Tell me the
speed of vessel-name 1 and the position of vessel-name 2 and
also send them to my email", as obtained by SpaCy parser.
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12. Step2 – 1st level Parsing
• Extraction of Vessels', Users' and Channels’ Names, using standard
template matching techniques.
• Date Extraction, using regular expressions and Natty library.
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13. Step3 – Classifier Preprocessing
• Tokenization, i.e. splitting a sentence into tokens and handling
punctuation.
• Removal of Unnecessary Words, such as stop-words (articles,
prepositions etc.) and numbers.
• Lemmatization, e.g. “gave”->”give”, “vessels“->”vessel” etc.
• Removal of Adverbial and Relative Clauses.
• Vectorization, using Tf-Idf schema and Scikit-Learn library.
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14. Step3 – Classifier Models
• Two strong SVM and Random Forest models, that generalize well.
• One weak Random Forest model, without removing the stop-words,
that can fit the training set.
• Use the weak classifier when at most one non-stop-word exists, else
the two strong models are applied.
• All models are trained using the Scikit-Learn library.
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15. Step4 – 2nd Level
Parsing
Different parameters are
demanded for the execution of
each Agent’s task. The scope of
this stage is the detection inside
the message of these parameters’
values.
Depending on the type of
parameters, a variety of algorithms
is employed, mostly syntax-based
or forms of Bag-of-Words template
matching.
Requested parameters for
this task:
• List of Quantities and
feature
• Vessel name
• Time Period
• Filters
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16. Evaluation - Effectiveness
Effectiveness reflects a bot's analytical capabilities.
• Correctness of commands' interpretation: the classifier achieves over
90% on validation sets.
• Correctness of tasks' type interpretation: question detection achieves
95% accuracy on 3372 sentences from the corpus of Noah et al. (1)
• Correctness of parameters' extraction: 77% on a collected dataset of 300
date formats.
• Expertise Knowledge is employed both in message analysis and during
the task execution.
(1) M. H. Noah A. Smith and R. Hwa, "Question generation as a competitive undergraduate course project", Proceedings of the NSF
Workshop on the Question Generation Shared Task and Evaluation Challenge, 2008.
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17. Evaluation – Efficiency & Users’ Satisfaction
Efficiency
METIS is robust to unexpected and noisy inputs grace to:
• Strong classifiers.
• Domain-specific message preprocessing.
• Embodiment of expertise knowledge.
Users’ Satisfaction
• Affect: User-friendly and pleasant. Very short familiarization time.
• Behavior: Respects privacy and personality.
• Accessibility: Answers back quickly, encouraging the user to ask another question.
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18. Thank you for
your attention.
For any question
just ask METIS!
nikos.gkanatsios@metis.tech
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