Chris Biemann: Text: Now in 2D — Lexical Expansion using Contextual Similarity

Chris Biemann will present a talk titled “Text: Now in 2D — Lexical Expansion using Contextual Similarity”.

Date: September 20, 2012
Time: 11:00am
Location: ETS, Conant Hall, Lounge A (directions | campus map).

ABSTRACT:

This talk introduces the metaphor of two-dimensional text. Starting from very basic concepts of structural linguistics, we define lexical expansion mechanisms that generate, for each term in context, a weighted list of possible expansions. While the mechanism is left unspecified by the metaphor, we use distributional similarity as a source for all-words unsupervised lexical expansion. Handling word sense ambiguity in the expansion mechanism will be discussed from two angles: Either a contextualized method can rank similar terms of the correct sense higher, or we can use a word sense induction clustering in order to aggregate over common features of the potential expansions.

This new representation has been successfully used in tasks like semantic text similarity and knowledge-based all-words word sense disambiguation. The key element of this representation and the method that computes it is that it can bridge lexical gaps and align passages that bear the same meaning without using the same words. Thus, it can be used as a basis technology for passage and answer scoring, and essay grading.

BIO:

Chris Biemann holds an MA and doctorate degree from the University of Leipzig, Germany. After his PostDoc at the semantic search start-up Powerset and subsequently the Microsoft Bing Search Engine, Chris became an assistant professor for language technology at the Technische Universität Darmstadt last year. His main research interests span unsupervised, knowledge-free acquisition, graph-based representations and algorithms, crowdsourcing, and big data for NLP applications. Currently, Chris is a visiting researcher at the IBM Watson Research Lab in Hawthorne NY, working with the Watson DeepQA team.