Welcome to the CrowdTruth blog!

The CrowdTruth Framework implements an approach to machine-human computing for collecting annotation data on text, images and videos. The approach is focussed specifically on collecting gold standard data for training and evaluation of cognitive computing systems. The original framework was inspired by the IBM Watson project for providing improved (multi-perspective) gold standard (medical) text annotation data for the training and evaluation of various IBM Watson components, such as Medical Relation Extraction, Medical Factor Extraction and Question-Answer passage alignment.

The CrowdTruth framework supports the composition of CrowdTruth gathering workflows, where a sequence of micro-annotation tasks can be configured and sent out to a number of crowdsourcing platforms (e.g. CrowdFlower and Amazon Mechanical Turk) and applications (e.g. Expert annotation game Dr. Detective). The CrowdTruth framework has a special focus on micro-tasks for knowledge extraction in medical text (e.g. medical documents, from various sources such as Wikipedia articles or patient case reports). The main steps involved in the CrowdTruth workflow are: (1) exploring & processing of input data, (2) collecting of annotation data, and (3) applying disagreement analytics on the results. These steps are realised in an automatic end-to-end workflow, that can support a continuous collection of high quality gold standard data with feedback loop to all steps of the process. Have a look at our presentations and papers for more details on the research.

Watson Innovation Course – Invited Lecture by Ken Barker, IBM Watson US

This week, the Watson Innovation course, a collaboration between the Vrije Universiteit, University of Amsterdam and IBM Netherlands, Centre for Advanced Studies (CAS) starts. The course offers a unique opportunity to learn about IBM Watson, cognitive computing and the meaning of such artificial intelligence systems in a real world and big data context. Students from Computer Science and Economics faculties join their complimentary efforts and creativity in cross-disciplinary teams to explore the business and innovation potential of such technologies.

This year, on 13th of November, Ken Barker from IBM Watson US will give an invited lecture. Here is an abstract of his invited lecture entitled “Question Answering Post-Watson”:

There is a long, rich history of Natural Language Processing and Question Answering research at IBM. This research achieved a significant milestone when the autonomous Question Answering system called “Watson” competed head-to-head with human trivia experts on the American television show, “Jeopardy!” Since that event, both Watson and QA/NLP research have barreled forward at IBM, though not always in the same direction.

In this talk, I will give a brief, biased history of Question Answering research and Watson at IBM, before and after the Jeopardy! challenge. But most of the talk will be a more technical presentation of our path of QA research “post-Watson”. The discussion will be in three parts: 1) Continuing research on traditional Question Answering technology beyond Jeopardy! 2) Work on transferring QA technology to Medicine and Healthcare; and 3) Recent research into exploratory, collaborative Question Answering against scientific literature.

Ken Barker Bio:

Ken Barker heads the Natural Language Analytics Department in the Learning Health Systems Organization at IBM Research AI. His current research examines the weaknesses of existing information gathering tools and applies Natural Language Processing to collaborative, exploratory question answering against scientific literature. Before joining IBM in 2011, he was a Research Faculty Member at the University of Texas at Austin, serving as Investigator on DARPA’s Rapid Knowledge Formation and Machine Reading Projects, as well as on Vulcan’s Digital Aristotle Project to build intelligent scientific textbooks. He was also an Assistant Professor of Computer Science at the University of Ottawa. His research there focused on Natural Language Semantics and Semi-Automatic Interpretation of Text.

Watson Innovation Course – Invited Lecture by Vanessa Lopez, IBM Ireland

This week, the Watson Innovation course, a collaboration between the Vrije Universiteit, University of Amsterdam and IBM Netherlands, Centre for Advanced Studies (CAS) starts. The course offers a unique opportunity to learn about IBM Watson, cognitive computing and the meaning of such artificial intelligence systems in a real world and big data context. Students from Computer Science and Economics faculties join their complimentary efforts and creativity in cross-disciplinary teams to explore the business and innovation potential of such technologies.

This year, on 16th of November, Vanessa Lopez from IBM Ireland Research will give an invited lecture. Here is an abstract of her invited lecture entitled “Cognitive solutions for Integrated Care”:

Cognitive technologies promise to have significant societal impact in domains where there is a need to transform multidisciplinary information into actionable services. From an industry perspective, the abundance ofdigital information gives an unprecedented opportunity to use data science to improve health and social care delivery.However,healthcare professionals have to quickly cope with large volume of information often scattered among unstructured case notes and health records to construct a care plan that addressessthe needs of the individual. In this talk, we look at the role of cognitive approaches to support care professionals to take better informeddecision,by capturing and interpreting patient-centric informationand learningfrom the actual practice of care professionals to suggest courses of action based on this holistic picture.With most of information still unstructured, we discuss the technologies, lessons learned and challenges behind this societal use case, in regards to knowledge acquisition, to find and combinemeaningful pieces of knowledge acrosssources with evidence for users’ information needs, and to facilitate intuitive human interaction, in which professionalsinteract with the system and the systems reacts and adapts its knowledge to give better suggestions,andfinally on how to validate the value of congitive systems with domain experts.

Vanessa Lopez Bio:

Vanessa Lopez is a researcher at IBM Research Ireland since 2012, where she investigates AI solutions for harnessing urban and web data as knowledge and to support users to query and find insights across data sources in a natural way, through a combination ofLinked Data, NLP and learning technologies for data integration. Her research has been applied to develop applications for smarter cities and Social and Health care to support care professionals to take better informed decisions.

Previous to joining IBM, she was a researcher at KMi (Open University) from 2003, where she investigated Question Answering interfaces for the Web of Data and received a PhD degree. She graduated in 2002 with a degree in computer engineer from the Technical University of Madrid (UPM), where she held an internship at the AI Lab. She has co-authored more than 40 publications in high impact conference and journals.

IBM Watson Masterclasses with VU Amsterdam and TU Delft

Why would decision makers attend these masterclasses?
To make informed business decisions on and around cognitive technologies, decision makers must understand the foundations, as well as the context, of these technologies.

What do we offer?
The IBM Benelux Center for Advanced Studies (CAS) teamed up with our long term collaborators in academia to deliver two 2-day masterclasses to educate decision makers about the technology. The first day of a masterclass will be at the university, covering the academic basics in an accessible way, while the second day is at IBM providing a more industrial angle of the topic.

The first masterclass, titled Foundations of Cognitive Computing, is delivered together with the Vrije Universiteit Amsterdam featuring renowned professors such Lora Aroyo, Guszti Eiben, and Frank van Harmelen (final list to be confirmed), but we will also have talks by IBM Research (Ken Barker), and demonstrations of past and present projects delivered locally by CAS. The preliminary dates for this Masterclass are 16-17 November 2017.

Topics covered (subject to change upon demand):

  • The past, present and future of Artificial Intelligence
  • Introduction to Cognitive Computing and Watson
  • How do Cognitive Systems learn?
  • Where is Watson now?
  • AI for the Masses (AI services in the cloud)
  • When AI Goes Bad (Ethics)
  • Demos and Corresponding Deep Dives

The second masterclass, titled The Internet of Everything and Everyone, is delivered with TU Delft, containing talks by prof. Geert-Jan Houben, dr. Alessandro Bozzon and several other researchers and students at the university, but also from IBM (John Cohn, Victor Sanchez, etc), CAS researchers and students. The date for for this masterclass are 7-8 December 2017.

Topics covered (subject to change upon demand):

  • Data collection for Cognitive systems:
    • big data,
    • sensor data,
    • human data
  • Solving real problems with IoT and with human computation
  • Data Science to connect machines and humans
  • Demos by students, faculty and IBM on how the technology can be used

What benefits can the masterclasses bring?
By gaining and understanding of what the technology is based on and what it can do, attendees can engage in deeper, more meaningful conversations about Cognitive, IoT, etc. They might become inspired to start projects using the technology, or perhaps the class can help them become convinced about the value a proposed IBM project.

Interested?  Contact us via casbnl@nl.ibm.com, or via Zoltan Szlavik and Benjamin Timmermans directly.

Lisbon Machine Learning Summer School 2017 – Trip Report

In the second half of July (20th of July – 27th of July) I attended the Lisbon Machine Learning Summer School (LxMLS2017). As every year, the summer school is held in Lisbon, Portugal, at Instituto Superior Técnico (IST). The summer school is organized jointly by IST, the Instituto de Telecomunicações, the Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa (INESC-ID), Unbabel, and Priberam Labs.

Around 170 students (mostly PhD students but also master students) attended the summer school. It’s important to mention that around 40% of the applicants are accepted, so make sure you have a strong motivation letter! For eight days we learned about machine learning with focus on natural language processing. The day was divided into 3 parts: lectures in the morning, labs in the afternoon and practical talks in the evening (yes, quite a busy schedule).

Morning Lectures

In general, the morning lectures and the labs mapped really well, first learn the notions and then put them into practice. During the labs we worked with Python and IPython Notebooks. Most of the labs had the base code already implemented and we just had to fill in some functions. However, for some of the lectures/labs this wasn’t that easy. I’m not going to discuss in detail the morning lectures but I’ll mention the speakers and their topics (also, the slides are available of the website of the summer school):

  • Mario Figueiredo: an introduction to probability theory which proved to be fundamental for understanding the following lectures.
  • Stefan Riezler: an introduction to linear learners using an analogy with the perceptual system of a frog, i.e., given that the goal of a frog is to capture any object of the size of an insect or worm providing it moves like one, can we build a model of this perceptual system and learn to capture the right objects?
  • Noah Smith: gave an introduction of sequence models such as Markov models and Hidden Markov models and presented the Viterbi algorithm which is used to find the most likely sequence of hidden states.
  • Xavier Carreras: talked about structured predictors (i.e., given training data, learn a predictor that performs well on unseen inputs) using as running example a named entity recognition task. He also discussed about Conditional Random Fields (CRF), approach that gives good results in such tasks.
  • Yoav Goldberg: talked about syntax and parsing by providing many examples of using them in sentiment analysis, machine translation and many other examples. Compared to the rest of the lectures, this one had much less math and was easy to follow!
  • Bhiksha Raj: gave an introduction to neural networks, more exactly convolutional neural networks (CNN) and recurrent neural networks (RNN). He started with the early models of human cognition, associationism (i.e., humans learn through association) and connectionism (i.e., the information is in the connexions and the human brain is a connectionist machine).
  • Chris Dyer: discussed about modeling sequential data with recurrent networks (but not only). He showed many examples related to language models, long short-term memories (LSTMs), conditional language models, among others. However, even if it’s easy to think of tasks that
 could be solved by conditional language models, most of the times the data does not exist, a problem that seems to appear in many fields and many examples.

Practical Talks

In the last part of the day we had practical talks or special talks of concrete applications that are based on the techniques learnt during the morning lectures. During the first day we were invited to attend a panel discussion named “Thinking machines: risks and opportunities” at the conference “Innovation, Society and Technology” where 6 speakers (Fernando Pereira – VP and Engineering Fellow at Google, Luís Sarmento – CTO at Tonic App’s, André Martins – Unbabel Senior researcher, Mário Figueiredo – Instituto de Telecomunicações at IST, José Santos Victor – president of the Institute for Systems and Robotics at IST and Arlindo Oliveira – president of Instituto Superior Técnico) in the AI field discussed about the benefits and risks of artificial intelligence and automatic learning. Here are a couple of thoughts:

  • Fernando Pereira: In order to enable people to make better use of technology, we need to make machines smarter at interacting with us and helping us.
  • André Martins pointed out an interesting problem: people spend time on solving very specific things but these are never generalized. -> but what if this is not possible?
  • Fernando Pereira: we build smart tools but only a limited amount of people are able to control them, so we need to build the systems in a smarter way and make the systems responsible to humans.

Another evening hosted the Demo Day, an informal gathering that brings together a number of highly technical companies and research institutions, all with the aim of solving machine learning problems through technology. There were a lot of enthuziastic people to talk to, many demos and products. I even discovered a new crowdsourcing platform, DefinedCrowd that soon might start competing with CrowdFlower and Amazon Mechanical Turk.

Here are some other interesting talks that we followed:

  • Fernando Pereira – “Learning and representation in language understanding”: talked about learning language representation using machine learning. However, machine understanding of language is not a solved problem. Learning from labeled data or learning with distant supervision may not yield the desired results, so it’s time to go implicit. He then introduced the work done by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin: Attention Is All You Need. In this paper, the authors claim that you do not need complex CNNs or RNNs models, but it’s enough to use attention mechanisms in order to obtain quality machine translation data.
  • Graham Neubig – “Simple and Efficient Learning with Dynamic Neural Networks”: dynamic neural networks such as DyNet can be used as alternatives to TensorFlow or Theano. According to Graham, here as some advantages of using such nets: the API is closer to standard Python/C++ and it’s easier to implement nets with varying structure and some disadvantages: it’s harder to optimize graphs (but still possible) and it’s also harder to schedule data transfer.
  • Kyunghyun Cho – “Neural Machine Translation and Beyond”: showed why sentence-level and word-level machine translation is not desired: (1) it’s inefficient to handle various morphological words variants, (2) we need good tokenisation for every language (not that easy), (3) they are not able to handle typos or spelling errors. Therefore, character-level translation is what we need because it’s more robust to errors and handles better rare tokens (which are actually not necessarily rare).

A Concentric-based Approach to Represent Topics in Tweets and News

[This post is based on the BSc. Thesis of Enya Nieland and the BSc. Thesis of Quinten van Langen (Information Science Track)]

The Web is a rich source of information that presents events, facts and their evolution across time. People mainly follow events through news articles or through social media, such as Twitter. The main goal of the two bachelor projects was to see whether topics in news articles or tweets can be represented in a concentric model where the main concepts describing the topic are placed in a “core”, and the concepts less relevant are placed in a “crust”. In order to answer to this question, Enya and Quinten addressed the research conducted by José Luis Redondo García et al. in the paper “The Concentric Nature of News Semantic Snapshots”.

Enya focused on the tweets dataset and her results show that the approach presented in the aforementioned paper does not work well for tweets. The model had a precision score of only 0.56. After a data inspection, Enya concluded that the high amount of redundant information found in tweets, make them difficult to summarise and identify the most relevant concepts. Thus, after applying stemming and lemmatisation techniques, data cleaning and similarity scores together with various relevance thresholds, she improved the precision to 0.97.

Quinten focused on topics published in news articles. When applying the method described in the reference article, Quinten concluded that relevant entities from news articles can be indeed identified. However, his focus was also to identify the most relevant events that are mentioned when talking about a topic. As an addition, he calculated a term frequency inverse document frequency (TF-IDF) score and an event-relation (temporal relations and event-related concepts) score for each topic. These combined scores determines the new relevance score of the entities mentioned in a news article. The improvements made improved the ranking of the events, but did not improve the ranking of the other concepts, such as places or actors.

Following, you can check the final presentations that the students gave to present their work:

A Concentric-based Approach to Represent News Topics in Tweets
Enya Nieland, June 21st 2017

The Relevance of Events in News Articles
Quentin van Langen, June 21st 2017