Nettsider med emneord «natural language processing»

Successful data science requires a mixture of statistics, computer science and domain knowledge. In DataScience@UiO, we bridge the first of these gaps by bringing statisticians and computer scientists from the Factulty of Mathematics and Natural Sciences together, and the other by bringing in industry-heavy research centers such as BigInsight and SIRIUS. In this blog-post, I want to say something about why I believe SIRIUS has a very important role to play in the current and future data science engagement at the University of Oslo.

A biweekly seminar run by and for LTG's master's students.

An important priority for LTG in recent years has been to create NLP resources for the Norwegian language, both in terms of modeling and datasets. This page provides an overview of our existing and ongoing projects to support Norwegian NLP.

NLP researchers both from and outside LTG are presenting their findings in an informal environment, followed by questions and discussions.

The SANT project develops resources for Sentiment Analysis for Norwegian Text. While coordinated by the Language Technology Group (LTG) at IFI/UiO, collaborating partners include NRK, Schibsted and Aller Media.

In the LogID group at ifi, a “visual query formulation” tool has been developed, that allows constructing database queries by a combination of ontology navigation, faceted search, and graph manipulation.  See the demo video below.

In this thesis, you will explore possibilities of enhancing the query construction possibilities by means of natural language processing technology.  Users will be able to specify initial versions of queries using natural language.  Queries can then be refined using e.g. a dialog system, or the existing query formulation tool. 

The master thesis will be carried out as a part of a recent EU project, called Optique (Scalable End-user Access to Big Data), and will give you opportunity to interact with top researchers all over the Europe.  It will also be co-supervised by ifi's LogID group and the Language Technology Group (LTG).

Ontologies are developed for representing requirements and specifications that can help domain experts and engineers do various tasks in the real world. Usually, as the ontologies are quite large, it takes a long time for human users to understand the ontologies and it is also quite difficult for machines to do reasoning on such large ontologies. The goal is that we can explore different machine-learning techniques to extract relevant parts of the ontology and reduce reasoning time.