JEP-TALN-RECITAL 2016, Paris, France

23rd French Conference on Natural Language Processing
31st "Journées d’Études sur la Parole"
18th Meeting of Student Researchers in Computer Science for Natural Language Processing

Inalco, Paris, 4-8 Juillet 2016

Conférenciers invités

Christian Chiarcos (Johann Wolfgang Goethe Universität Frankfurt a. M.) - Mardi 10h00-11h00 (CI1) - Président de session : Emmanuel Morin

Corpora and Linguistic Linked Open Data: Motivations, Applications, Limitations

Linguistic Linked Open Data (LLOD) is a technology and a movement in several disciplines working with language resources, including Natural Language Processing, general linguistics, computational lexicography and the localization industry. This talk describes basic principles of Linguistic Linked Open Data and their application to linguistically annotated corpora, it summarizes the current status of the Linguistic Linked Open Data cloud and gives an overview over selected LLOD vocabularies and their uses. A resource constitutes Linguistic Linked Open Data if it is published in accordance with the following principles:
  1. The dataset is relevant for linguistic research or NLP algorithms.
  2. The elements in the dataset should be uniquely identified by means of a URI.
  3. The URI should resolve, so users can access more information using web browsers.
  4. Resolving an LLOD resource should return results using web standards such as Resource Description Framework (RDF).
  5. Links to other resources should be included to help users discover new resources and provide semantics.
  6. Data should be openly licensed using licenses such as the Creative Commons licenses.
Criterion (1) defines linguistic(ally relevant) data, criteria (2-5) define linked data, criterion (6) defines open data, their combination thus yields Linguistic Linked Open Data. The primary benefits of LLOD have been identified as:
  • Representation: Linked graphs are a more flexible representation format for linguistic data
  • Interoperability: Common RDF models can easily be integrated
  • Federation: Data from multiple sources can trivially be combined
  • Ecosystem: Tools for RDF and linked data are widely available under open source licenses
  • Expressivity: Existing vocabularies help express linguistic resources.
  • Semantics: Common links express what you mean.
  • Dynamicity: Web data can be continuously improved.
I specifically focus on linguistically annotated corpora and discuss the potential of Linked Data in relation to four standing problems in the field:
  1. representing highly interlinked corpora (e.g., multi-layer corpora, annotated parallel corpora),
  2. integrating corpora with lexical resources available from the web of data,
  3. facilitating annotation interoperability using terminology resources available from the web of data, and
  4. streamlining data manipulation processes in a modular and domain-independent fashion.
These aspects will be discussed in relation to two selected resources from both general linguistics and Natural Language Processing. Finally, the talk will discuss some of the challenges that LLOD is still facing in both areas.

Mark Liberman (LDC & University of Pennsylvania) - Jeudi 16h30-17h30 (CI2) - Président de session : Guillaume Gravier

From Human Language Technology to Human Language Science

Thirty years ago, in order to get past roadblocks in Machine Translation and Automatic Speech Recognition, DARPA invented a new way to organize and manage technological R&D: a ``common task'' is defined by a formal quantitative evaluation metric and a body of shared training data, and researchers join an open competition to compare approaches. Over the past three decades, this method has produced steadily improving technologies, with many practical applications now possible. And Moore's law has created a sort of digital shadow universe, which increasingly mirrors the real world in flows and stores of bits, while the same improvements in digital hardware and software make it increasingly easy to pull content out of the these rivers and oceans of information.
It's natural to be excited about these technologies, where we can see an open road to rapid improvements beyond the current state of the art, and an explosion of near-term commercial applications. But there are some important opportunities in a less obvious direction. Several areas of scientific and humanistic research are being revolutionized by the application of Human Language Technology. At a minimum, orders of magnitude more data can be addressed with orders of magnitude less effort -- but this change also transforms old theoretical questions, and poses new ones. And eventually, new modes of research organization and funding are likely to emerge.