This document discusses using deep learning techniques for information extraction from natural language text. It provides an overview of Pankaj Gupta's background and research focus on applying machine learning and deep learning to natural language processing tasks like entity and relation extraction. It then describes supervised deep learning methods that have been used to build state-of-the-art systems for jointly extracting entities, relations, and knowledge graph triples from text.

1. Deep Learning for Information Extraction in
Natural Language Text
Pankaj Gupta
CT RDA BAM MIC-DE
Young Research Forum 2017 | Siemens AG, Germany
Siemens Corporate TechnologyRestricted © Siemens AG 2016

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About Me @CT RDA BAM MIC-DE !!
Nov, 2015
Present
➢ LinkedIn: https://www.linkedin.com/in/pankaj-gupta-6b95bb17/
➢ Google Scholar: https://scholar.google.com/citations?user=_YjIJF0AAAAJ&hl=en
Research Focus: Machine Learning (Deep Learning) techniques to solve Natural Language Processing (NLP) tasks
• Bachelor of Technology in Computer Science, Amity University, India
• Bachelor Internship: Queen’s University, Belfast Northern Ireland UK
• Publications: 2
• Senior Software Engineer at Wipro and Aricent Technologies, IndiaJun, 2010-
Sept, 2013
• Master of Science in Informatics, Technical University of Munich (TUM), Germany
• Master’s Thesis (Siemens + LMU): Deep Learning Methods for the Extraction of Relations in Natural Language Text
• Publications: Deep Learning/NLP focused: 2 and Machine Learning based: 2
Oct, 2013
Nov, 2015
• PhD at CT-RDA-BAM-MIC-DE Siemens AG and at LMU (CIS) Munich Germany
• Advisor(s): Dr. Bernt Andrassy (Siemens AG) and Prof. Dr. Hinrich Schütze (CIS LMU)
• PhD Thesis: Deep Learning Methods for Information Extraction in Natural Language Text
• Publications: Published: 1, Review: 3, Filed 3 patents
Oct, 2006-
April, 2010

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Information Extraction(IE) in Natural Language Text
• Entity Extraction
• Relation Extraction
• Structure the unstructured text
• Knowledge Graph Construction
• In web search, retrieval, Q&A, etc.
Information Extraction
Entity Extraction: Detect entities such as person, organization, location, product, technology, sensor, etc.
Relation Extraction: Detect relation between the given entities or nominals
End-to-End Knowledge Base Population
Text Documents Knowledge GraphIE Engine
SensorSensor
Competitor-of
Sensor

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Supervised Deep Learning Techniques in Information Extraction
• Natural language is sparse and noisy
• Better Representation Learning
• Build state-of-the-art entity and relation
extraction systems with Neural
Networks to extract triples (entity1,
entity2, relation)
Challenges and Motivation Our Pipelined Deep Learning System for Entity and Relation Extraction
Motivation to build the state-of-the-art Deep Learning system(s) for Smart Data Web project
• Learn from noisy text
• Better approximate the highly non-
linear arbitrary function
• Pattern and Representation Learning,
especially in Language Models with no
explicit feature extraction
Benefits of Deep Learning in NLP
Extended Convolutional Neural Network2
Connectionist Bi-directional Neural Network2
Ranking Recurrent Neural Network (R-RNN)1
Entity/Concept Extraction Relation Extraction
TriplesText
(1) N.T.Vu, P. Gupta, H. Adel, H. Schütze. Bi-directional RNN with Ranking Loss for SLU. In ICASSP2016.
(2) N.T.Vu, H. Adel, P. Gupta, H. Schütze. Combining Recurrent and Convolutional Neural Networks for Relation Classification. In NAACL2016.
( Siemens,
Competitor-of,
ABB )
Siemens,
ABB
Competitor-of

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Supervised Deep Learning in Joint Entity and Relation Extraction
• Entity and relation inter-dependencies
• Multi-tasking to jointly learn entity and
relation representations and patterns
• State-of-the-art system published3 for
joint entity and relation extraction
Motivation: Joint/Multi-task Learning Joint/Multi-task Neural Learning for End-to-End Entity and Relation Extraction
Our State-of-the-Art system based on Neural Architectures for Joint Entity and Relation Extraction
Neural Information Extraction System3
Text Documents
(3) P. Gupta, A. Bernt, H. Schütze. Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction.
In COLING2016.
Supplier-ofSupplier-of
Supplier-of
Competitor-of
Sensor
Sensor
Sensor Sensor

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Deep Learning and NLP Applications at Siemens
Public Domain: Web Semantic Search and Retrieval
Application of Information Extraction in Public and Industrial domains
Industrial Domain: Slot Filling for Product in Tender Documents
Rectifier
RATED CURRENT: ??
OUTPUT VOLTAGE: ??
OVERLOAD : ??
Query-Input Tender Documents,
Service Reports
IE System
Query-Output
Rectifier
RATED CURRENT: 2666 A
OUTPUT VOLTAGE: 1500 V
OVERLOAD: 2 h

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Deep Learning and NLP Applications for TimeLines at Siemens
Public Domain: TimeLine Generation from Biographies
Application of Information Extraction in Public and Industrial domains
Industrial Domain: TimeLine of Product for Historical Analysis and Monitoring (Future Work)
Timeline of -
➢ Industrial products
➢ Industrial re-organization
➢ Business Strategy vs Profit
Bloomberg-Biographies,
European Data Forum
Digitaleweltmagazin.de
Intelligent TimeLine
Extraction System