Extracting meaning from data

Doctor

Extracting meaning from data is the central business of the information era. ML Market is a European consortium of leading researchers that span a range of areas in information processing, data analysis, statistics and machine learning.

ML Market groups are formed from world-leading research groups within the Pascal European network that also actively engage to find business solutions to challenging real-world problems. ML Market exists to promote the academic and industrial expertise of its researchers and provides a platform to engage and broker industrial contacts.

Case Studies

Letting stones go unturned

Wéiqí, Baduk or simply, Go. Over two thousand years old, this challenging game of strategy and tactics has taught warriors, monks and intellectuals to focus, concentrate and plan. But a program is beginning to change our views of what is possible. In computer competitions this multi-armed bandit has stolen first prize more often than any other. It’s MoGo, or Monte Carlo Go.

The Desktop Doctor

DoctorHundreds of years of medical experience. An infinite patience and the ability to take every symptom into account. Precise and logical, up-to-date, and never short on ideas. All just casually sitting on your doctor’s desk. It may not have much of a bedside manner, but then its job is not to meet patients.

Winestein, the computer with taste for wine

Doctor A common problem when you organize a dinner or when you are in a restaurant and you get the wine list: what wine goes best with your dish?

Winewinewine.com is a web-portal for wine. One of its distinguishing features is Winestein, the on-line sommelier. You can enter any dish of your choice by entering ingredients and cooking method. Then winestein will advise matching wines.

Topics

Statistical Machine Translation

Half of the EU citizens are not able to hold a conversation in a language other than their mother tongue, let alone to conduct a negotiation, or interpret a law. In a time of wide availability of communication technologies, language barriers are a serious bottleneck to European integration and to economic and cultural exchanges in general. More effective tools to overcome such barriers, in the form of software for machine translation and other cross-lingual textual information access tasks, are in strong demand.

Handwritten Text Recognition

Many documents used every day are handwritten documents, as for example, postal addresses, bank cheques, medical prescriptions, a big quantity of historical documents, an important part of the information gathered by forms, etc. In many cases it would be interesting to have these documents in digital form rather than paper based, in order to provide new ways to indexing, consulting and working with these documents.

Handwriting text recognition (HTR) can be defined as the ability of a computer to transform handwritten input represented in its spatial form of graphical marks into equivalent symbolic representation as ASCII text. Usually, this handwritten input comes from sources such as paper documents, photographs or electronic pens and touch-screens.

Text mining

Text mining, sometimes alternately referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the divining of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities).