II-SDV 2014 A New Approach to Flexible, Meaning-Rich Document Parsing (Paul Barba -- Lexalytics, USA)

•

1 gostou•3,850 visualizações

Dr. Haxel Consult

Software Tecnologia Educação

Lexalytics is
• A software company
• We sell the “Salience Engine”
• Salience is a Text Analytics Engine
that fits into your software,
services, or applications
• What we ship is a set of libraries
and configuration files
© 2014 Lexalytics Inc. All rights reserved. lexalytics.com
2
S A L I E N C E 5 . 2

Market Proven IP: 11 Years of R&D
© 2014 Lexalytics Inc. All rights reserved. lexalytics.com
3
Approximately 3 Billion documents/day go through Salience.
2/2012: Mobile Functionality
– Port the Salience engine to
Android mobile devices
11/2010: Salience 4.4
released, includes support
for first non-English language
(French)
10/2011: Salience v5.0
incorporates innovative
Concept Matrix functionality
06/2012: Salience v5.1
released, expansion of
available options and
optimized sentiment analysis
functionality
08/2013: Chinese language
released; multi-lingual
support in 6 languages
Q4/2014: Salience v6 – new
underpinnings, easier tuning,
and “Intent” extraction
2004: Lexalytics launches
first commercial text and
sentiment analysis engine,
Salience v1.0
10/2008: Salience 4.0
released, based on
maximum entropy model for
detection and labeling of
novel entities
08/2010: Salience 4.3 to
include custom handling of
Twitter and micro-blog
content
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Q4/2014: Salience v5.2
released with various
feature enhancements

A Multi-lingual World WLOA (With Lots of Acronyms) and
Context Everywhere
4
Lexalytics Salience Training prepared for Analytics8
NLP
• New Labor Party
• National Landcare Program
• Network Layer Packet
• NeuroLinguistic Programming
• Wicked
• Sick
• Hack

Always running to catch up…
5
Lexalytics Salience Training prepared for Analytics8

God Bless Moore’s Law and Librarians
7
Lexalytics Salience Training prepared for Analytics8

Unsupervised learning is the key
8
Lexalytics Salience Training prepared for Analytics8

Meaning Matters
9
Lexalytics Salience Training prepared for Analytics8
It’s not that I don’t like tea I just prefer coffee

Meaning Matters
10
Lexalytics Salience Training prepared for Analytics8
Jane will be joining already with a search experta team

Meaning Matters
11
Lexalytics Salience Training prepared for Analytics8
Jane will be joining a team already with some search experience

Episode 4: A New Hope
12
Lexalytics Salience Training prepared for Analytics8
Sentence POS Tagger Chunker
Rules
File
Candidate
Parse
Terms

Jane and her team
<Jane will be joining a team already
with search experience>
• Pos Tag
<Jane_NNP will_MD be_VB
joining_VBP a_DT team_NN
already_RB with_PP search_JJ
experience_NN>
• Chunk
<Jane> <will be joining> <a team>
<already with search experience>
13
• Extract possible links
Jane => will be joining
will be joining => a team
a team => already with search
experience
will be joining => already with search
experience
Jane => already with search
experience.
Lexalytics Salience Training prepared for Analytics8

Matrices of Meaning
14
Lexalytics Salience Training prepared for Analytics8

Matrix Math
15
Lexalytics Salience Training prepared for Analytics8
All noun phrases
All
verb
phrases

Now look at how easy it is
• <Do you want me to get anything else while I go to the
store for milk?>
• pos tag and chunk it.
<Do> <you> <want> <me> <to get> <anything else>
<while> <I> <go> <to the store> <for milk>
16
Find the possible links.
do want
you want
want me
you to get
want to get
me to get
to get anything else
want while
to get while
while go
I go
go to the store
I to the store
get to the store
want to the store
to the store for milk
go for milk
want for milk
Lexalytics Salience Training prepared for Analytics8

A world of new possibilities
17
Lexalytics Salience Training prepared for Analytics8

Mais conteúdo relacionado

Semelhante a II-SDV 2014 A New Approach to Flexible, Meaning-Rich Document Parsing (Paul Barba -- Lexalytics, USA)

Essence: A Common Ground for Flexible Methods

Ed Seidewitz

Lets learn coding LLC

NahidHasan115

Learn How Selenium And Jenkins Fit In DevOps | Edureka Live

Edureka!

Data Driven Framework In Selenium Webdriver | Data Driven Testing | Selenium ...

Edureka!

Slack's Developer Relations Strategy - CMX Summit West 2016

CMX

Software quality is the backbone of any successful application, but too often takes the backseat to delivering on time. There have been significant changes in the way software gets developed and released over the past few decades, and the pressing need for faster cycles has driven the adoption of methodologies like DevOps. DevOps allows for the continuous delivery of software with minimum disruption to existing infrastructures by increasing the collaboration between development and operations teams. However, adopting a DevOps mentality is not enough. More often than not, quality is compromised when the goal is to get the software to customers as soon as possible. No team should have to take that risk! On Thursday, September 6th in The PorterShed Galway, some of the SmartBar team held a meet up, where attendees had the opportunity to become part of the DevOps pipeline and learn how to efficiently adopt a DevOps model without compromising software quality.

DevOps: 6 Steps to Go Faster, Build Better and Avoid Disaster

SmartBear

Puppet Camp Paris 2014: Achieving Continuous Delivery and DevOps with Puppet

Puppet

Achieving Continuous Delivery with Puppet

Devoteam Revolve

Best Tools You Should Use For SEO in 2021-NSDM INDIA

Abbas Dudhwala

The trend of laravel application development will never end!

Concetto Labs

Top Seven Business Benefits of DevOps

Urolime Technologies

Parse Server Open Source

George Batschinski

Module-1_Getting-Started_with_selenium_and_Java_basics.pdf

devika266518

Industry stories on agile, scrum and kanban

Business901

SAP HANA SPS09 - Text Analysis

SAP Technology

Are you a: - University student or fresh graduate wishing to pursue a career in DevOps and want to prepare for it? - Software Engineer (developer, tester, etc.) who is curious about DevOps? - Software Engineer (developer, tester, etc.) wishing to switch from his/her current role to a DevOps related role? This session is just for you! Check out the video on YouTube at https://www.youtube.com/watch?v=yYWEOdORH40

DevOps for absolute beginners

Ahmed Misbah

DevOps the NetApp Way: 10 Rules for Forming a DevOps Team

NetApp

The Top 5 DevOps FAQs Finally Answered

Flexsin

With the explosion of new frameworks, a mountain of automation, and our applications distributed across hundreds of services in the cloud, the level of complexity in software development is growing at an insane pace. With increased complexity comes increased costs and risks. When diagnosing unexpected behavior can take days, weeks, or sometimes months, all while our release is on the line, our projects plunge into chaos. In the invisible world of software development, how do we identify what's causing our pain? How do we escape the chaos? Janelle Klein presents a novel approach to measuring the chaos, identifying the causes, and systematically driving improvement with a data-driven feedback loop. Rather than measuring the problems in the code, Janelle suggests measuring the "friction in Idea Flow", the time it takes a developer to diagnose and resolve unexpected confusion, which disrupts the flow of progress during development. With visibility of the symptoms, we can identify the cause—whether it's bad architecture, collaboration problems, or technical debt. Janelle discusses how to measure Idea Flow, why it matters, and the implications for our teams, our organizations, and our industry.

Identify Development Pains and Resolve Them with Idea Flow

TechWell

Interns What Is DevOps

Aaron Blythe

Semelhante a II-SDV 2014 A New Approach to Flexible, Meaning-Rich Document Parsing (Paul Barba -- Lexalytics, USA) (20)

Essence: A Common Ground for Flexible Methods

Lets learn coding LLC

Learn How Selenium And Jenkins Fit In DevOps | Edureka Live

Data Driven Framework In Selenium Webdriver | Data Driven Testing | Selenium ...

Slack's Developer Relations Strategy - CMX Summit West 2016

DevOps: 6 Steps to Go Faster, Build Better and Avoid Disaster

Puppet Camp Paris 2014: Achieving Continuous Delivery and DevOps with Puppet

Achieving Continuous Delivery with Puppet

Best Tools You Should Use For SEO in 2021-NSDM INDIA

The trend of laravel application development will never end!

Top Seven Business Benefits of DevOps

Parse Server Open Source

Module-1_Getting-Started_with_selenium_and_Java_basics.pdf

Industry stories on agile, scrum and kanban

SAP HANA SPS09 - Text Analysis

DevOps for absolute beginners

DevOps the NetApp Way: 10 Rules for Forming a DevOps Team

The Top 5 DevOps FAQs Finally Answered

Identify Development Pains and Resolve Them with Idea Flow

Interns What Is DevOps

Mais de Dr. Haxel Consult

AI-SDV 2022: Henry Chang Patent Intelligence and Engineering Management

Dr. Haxel Consult

Knowledge Graphs are an increasingly relevant approach to store detailed knowledge in many domains. Recent advances in NLP allow to enrich Knowledge Graphs through automated analysis of large volumes of literature, reducing a lot the efforts in traditional manual information capturing. In our presentation we report the approach taken in a project with partner Fraunhofer SCAI in the life sciences where a knowledge graph organising detailed facts about psychiatric diseases has been computed. Information of cause-effect relations between proteins, genes, drugs and diseases has been encoded in the BEL (Biological Expression Language) and imported into a Graph database to approach an indication-wide Knowledge Graph for the selected therapeutic area. Ultimately, updating the graph will amount to just rerunning the analysis on the newly published literature.

AI-SDV 2022: Creation and updating of large Knowledge Graphs through NLP Anal...

Dr. Haxel Consult

In 2019 the UK was the first major economy to embrace a legal obligation to achieve net zero carbon emissions by 2050. More broadly, the 2021 UK Innovation Strategy sets out the UK government’s vision to make the UK a global hub for innovation by 2035 with a target of increasing public and private sector R&D expenditure to 2.4% of GDP to support the UK being a science superpower with a world-class research and innovation system. IP rights create an incentive for R&D which ultimately leads to innovation. Analysis and insights from IP data can therefore help provide a better understanding of how the IP system is being used and where and what innovation is taking place. Research and analysis of IP data is a key input to the ongoing work of the UKIPO’s Green Tech Working Group which seeks to: further the UK’s status as a global leader by making the UK’s IP environment the best for innovating green technology; develop and deliver IP policies to support government’s ambition on climate change and green technologies; and to help innovators best protect and commercialise their green tech innovations both at home and internationally. The UKIPO has been developing a broad portfolio of ‘green’ IP analytics research. A series of patent analytics reports have been published looking at green technologies, and analysis of how the UK’s Green Channel scheme for accelerated processing of green patent applications has been conducted. Patents have been used to identify technological comparative advantage within different green technologies at a country level, and new insights uncovered by mapping green technology patents to the UN Sustainable Development Goals (SDGs). Trade mark data provides a timeliness and closeness to market factor that patent data does not, and complementary trade mark analysis of UK ‘green’ trade marks, identified using a machine learning algorithm, provides a commercialisation angle to our research.

AI-SDV 2022: The race to net zero: Tracking the green industrial revolution t...

Dr. Haxel Consult

Word embeddings, deep learning, transformer models and other pre-trained neural language models (sometimes recently referred to as "foundational models") have fundamentally changed the way state-of-the-art systems for natural language processing and information access are built today. The "Data-to-Value" process methodology (Leidner 2013; Leidner 2022a,b) has been devised to embody best practices for the construction of natural language engineering solutions; it can assist practitioners and has also been used to transfer industrial insights into the university classroom. This talk recaps how the methodology supports engineers in building systems more consistently and then outlines the changes in the methodology to adapt it to the deep learning age. The cost and energy implications will also be discussed.

AI-SDV 2022: Accommodating the Deep Learning Revolution by a Development Proc...

Dr. Haxel Consult

In the patent domain, all types of issues, from very specific search requirements to the linguistic characteristics of the text domain, are accentuated. Consequently, to develop patent text mining tools for scientists and patent experts, we need to understand their daily work tasks, as well as the linguistic character of the text genre (i.e., patentese). Patent text is a mixture of legal and domain-specific terms. In processing technical English texts, a multi-word unit method is often deployed as a word-formation strategy to expand the working vocabulary, i.e., introducing a new concept without the invention of an entirely new word. This productive word formation is a well-known challenge for traditional natural language processing tools utilizing supervised machine learning algorithms due to limited domain-specific training data. Deep learning technologies have been introduced to overcome the reduction in performance of traditional NLP tools. In the Artificial Researcher technologies, we have integrated explicit and implicit linguistic knowledge into the deep learning algorithms, essential for domain-specific text mining tools. In this talk, we will present a step-by-step process of how we have developed the mentioned text mining tools. For the final outline, we will also demonstrate how these tools can be integrated in a cross-genre passage retrieval system, based on a technology from 2016 that still holds the state-of-the-art within the patent text mining research community in 2022.

AI-SDV 2022: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...

Dr. Haxel Consult

In 2013 we witnessed an evolutionary change in the NLP field evolved thanks to the introduction of space embeddings that, with the use of deep learning architectures, achieved human-level performances in many NLP tasks. With the introduction of the Attention mechanism in 2017 the results were further improved and, as result, embeddings are quickly becoming the de facto standards in solving many NLP problems. In this presentation, you will learn how generate and use space embedding for search purposes and provide comparison metrics to more traditional relevance-based search engines. Moreover, I will provide some initial results on a paper currently under review that provides an insight on hyperparameter tuning during the generation of embeddings.

AI-SDV 2022: Embedding-based Search Vs. Relevancy Search: comparing the new w...

Dr. Haxel Consult

AI-SDV 2022: Rolling out web crawling at Boehringer Ingelheim - 10 years of e...

Dr. Haxel Consult

AI-SDV 2022: Machine learning based patent categorization: A success story in...

Dr. Haxel Consult

AI-SDV 2022: Machine learning based patent categorization: A success story in...

Dr. Haxel Consult

It is relatively easy for a human to read a document and quickly figure out which concepts are important. However, this task is a difficult challenge for a machine. During the past few decades, there have been two main approaches for concept identification: Natural Language Processing and Machine Learning. During the early part of this century, Machine Learning made great strides as new techniques came into wider use (SVM’s, Topic Modeling, etc..). Sensing the competition, Natural Language Processing responded with deployment of new emerging techniques (sematic networks, finite state automata, etc..). Neither approach has completely solved the WHAT problem. Advances in Artificial Intelligence have the potential to significantly improve the situation. Where AI is making the most impact is as an enhancement to make Machine Learning and Natural Language Processing work better and, more importantly, work together. This presentation looks at some of this history and what might happen in the future when we blend the interpretation of language with pattern prediction.

AI-SDV 2022: Finding the WHAT – Will AI help? Nils Newman (Search Technology,...

Dr. Haxel Consult

Trademarks serve as key leading indicators for innovation and economic growth. As the vanguards of new and expanding enterprises, trademarks can be used to study entrepreneurship and shifting market demands in response to varying economic factors. This responsiveness has been seen as recently as the COVID-19 pandemic, where trademark research revealed key insights about business reaction to the global upheaval. At CIPO, we have been delving more deeply than ever before into trademark analysis by leveraging cutting-edge natural language processing (NLP) tools to derive actionable business intelligence from trademark data. In this presentation, we present a survey of NLP in use at CIPO and the insights we have learned applying them. These insights include COVID-19 responses, line-of-business trends based on firm characteristics, and more. We also discuss ongoing and future trademark research projects at CIPO. These projects include emerging technology detection methods and high-resolution trademark classification systems. We conclude that artificial intelligence-enhanced tools like NLP are key components of future exploitation of trademark data for business and economic intelligence.

AI-SDV 2022: New Insights from Trademarks with Natural Language Processing Al...

Dr. Haxel Consult

In our customer projects involving automated document processing, we often encounter document types providing crucial data in the form of tables. While established text analytics algorithms are usually optimized to operate on running text, they tend to produce rather poor results on tables as they do not capture the non-sequential relations inside them (e.g. interpret the content of a table cell relative to its column title, interpret line breaks inside a cell differently from line breaks between cells or rows). While there are elaborate information extraction products in the market for a few highly specific types of tabular documents, there is no general approach out there. The main cause for this is the fact that table structures can be encoded by a heterogenous range of layout means (e.g. column boundaries can be signaled by lines vs. aligned text vs. white space). In this talk, we will illustrate several solutions that we have developed for a range of challenges occurring in this context, both for scanned and digitally generated documents.

AI-SDV 2022: Extracting information from tables in documents Holger Keibel (K...

Dr. Haxel Consult

Most scientific journals request, that the complete set of research data is published simultaneously with the peer-reviewed paper. The publication of the research data usually is carried out as so-called "Supplementary Material", attached to the original paper, or on a "Research Data Repository". Both forms have in common, that the data is published usually unstructured and not in an uniform machine processable format. This makes its further use in electronic tools for AI or data mining unnecessarily difficult or even impossible. A concept is presented, in which the data is digitally recorded, following the principle of FAIR data, as part of the publication process. This digital capture makes the data available to the scientific community for easy use in data mining and AI tools. The data in the repository contains links to the publication to document its origin. The concept is applicable for preprints, peer-review papers, diploma and doctoral theses and is particularly suitable for open access publications. Moreover, the presentation highlights correspondent activities, which were released in scientific publications recently.

AI-SDV 2022: Scientific publishing in the age of data mining and artificial i...

Dr. Haxel Consult

AI-SDV 2022: AI developments and usability Linus Wretblad (IPscreener / Uppdr...

Dr. Haxel Consult

How do you find video when you only have sparse data? While you can wander the stacks (if you can still find open stacks) for inspiration, video either physical or digital, is difficult to discover. Wandering the virtual stacks is, well, virtually impossible. Discovery platforms on the whole have not replicated the inspirational experience of wandering the stacks. More companies are using archivable video for internal communication of the various research projects, product developments, test results, and more that are being considered, in progress, or completed. Showing how an experiment was conducted can convey considerably more information that is very difficult to communicate via text. How do you find a company video that might be helpful for your project? A case study is presented of the problems and the solutions that were implemented by a large, multinational chemical company. A suite of content discovery technologies was used including a video to text to tagging system connected to their documents database and automatically indexed using several chemical as well as conceptual systems (rule-based, NLP, inference engine). To build the system and support the manuscript and video submission there is a metadata extraction program which pulls and inserts the metadata into the submission forms so the author can move quickly through that process.

AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...

Dr. Haxel Consult

Copyright Clearance Center A pioneer in voluntary collective licensing, CCC (Copyright Clearance Center) helps organizations integrate, access, and share information through licensing, content, software, and professional services. With expertise in copyright and information management, CCC and its subsidiary RightsDirect collaborate with stakeholders to design and deliver innovative information solutions that power decision-making by helping people integrate and navigate data sources and content assets. CCC recently acquired the assets and technology of Deep SEARCH 9 (DS9), a knowledge management platform that leverages machine learning to help customers perform semantic search, tag content, and discover new insights.

AI-SDV 2022: Copyright Clearance Center

Dr. Haxel Consult

Lighthouse IP is the world’s leading provider of intellectual property content. The core business of Lighthouse IP is sourcing and creating content from the world’s most challenging authorities. Specialized in IP data, Lighthouse IP provides over 160 countries coverage for patents, over 200 authorities for trademarks and over 90 authorities for designs. Lighthouse IP data is available via several partners. The company is headquartered in Schiphol-Rijk in the Netherlands and has offices in the United States, China, Thailand, Vietnam, Egypt, Indonesia and Belarus. Globally a team of 150 experts works on the creation of this unique data collection.

AI-SDV 2022: Lighthouse IP

Dr. Haxel Consult

CENTREDOC was created in 1964 as the technical information center of the swiss watchmaking industry. Building on a strong team of engineers, CENTREDOC now offers a complete range of services and solutions for the monitoring of strategic, technological and competitive information. CENTREDOC is also a leader in the research of patent, technical and business intelligence, and offers consulting expertise in the implementation of monitoring solutions.

AI-SDV 2022: New Product Introductions: CENTREDOC

Dr. Haxel Consult

AI-SDV 2022: Possibilities and limitations of AI-boosted multi-categorization...

Dr. Haxel Consult

What if there was a platform where literature, conference abstracts, patents, clinical trials, news, grants and other sources were fully integrated? What if the data would be harmonized, enriched with standardized concepts and ready for analysis? After building our patent analytics platform we didn’t stop dreaming and built our big data analytics platform by semantically integrating text-rich, scientific sources. In my presentation I will talk about what we built and why we built it. And, of course, I will also address the challenges and hurdles along the way. Was it worth it and what comes next? Let’s talk about it!

AI-SDV 2022: Big data analytics platform at Bayer – Turning bits into insight...

Dr. Haxel Consult

Mais de Dr. Haxel Consult (20)