Event Date: Wednesday, 15 March, 2017, 10 a.m.
Location: Via Santa Maria, 36, Pisa, PI, Italia [2nd floor seminar room]
Speaker: Prof. Chris Biemann (University of Hamburg)
Title: Adaptive Natural Language Processing with Graph-based Distributional Semantics
Abstract: In this talk, I will motivate an adaptive approach to natural language processing, where NLP components get smarter through usage over time, following a ‘cognitive computing’ approach to natural language processing. With the help of recent research prototypes, three stages of data-driven adaptation will be illustrated: feature/resource induction, induction of processing components and continuous data-driven learning.
Manny of these approaches have been implemented with a scalable graph-based solution to distributional semantics, which belongs to the family of ’count-based’ DSMs, keeps its representation sparse and explicit, and thus fully interpretable. To put this into perspective, I will highlight some important differences between sparse graph-based and dense vector approaches to DSMs: while dense vector-based models are computationally easier to handle and provide a nice uniform representation, they lack interpretability, provenance and robustness. On the other hand, graph-based sparse models have a more straightforward interpretation, handle sense distinctions more naturally and can straightforwardly be linked to knowledge bases, while lacking the ability to compare arbitrary lexical units and a compositionality operation. Since both representations have their merits, I opt for exploring their combination in the outlook.
Chris Biemann obtained his doctorate in Computer Science/Natural Language Processing in 2007 from the University of Leipzig, before joining the San-Francisco-based semantic search startup Powerset, which was acquired by Microsoft to form the Bing.com search engine. In 2011, he got appointed as assistant professor for language technology in the computer science department at TU Darmstadt; since October 2016, Chris Biemann is professor for language technology at the University of Hamburg. His current research is focused on adaptive natural language processing in the cognitive computing paradigm, web-scale statistical semantic methods, machine learning from crowdsourcing signals and on applications in the humanities and social sciences.