Natural language research announcements

[Chang Ming-Wei <mchang21 AT uiuc.edu>]

Dear faculty and students,

    
A Ph.D. candidate of CS department, Alexandre Klementiev, will give a talk (details
below) for the AIIS seminar at 4:00 pm, Apr 2nd (this Thursday). The
room number is 3405. Hope to see you there!


Title:

Learning with Incidental Supervision

Abstract:

Moving toward understanding and automatic generation of natural
human languages requires a toolbox of core capabilities.  It is well
accepted today that it is essentially impossible to manually encode
many of these capabilities without the aid of machine learning
techniques, which automatically acquire them from available natural
language data.  Corpus-based supervised learning has emerged as the
dominant approach, and it relies crucially on the availability of
labeled data.  However, while unsupervised data is usually plentiful,
its annotation is a laborious process for a number of realistic Natural
Language Processing tasks, especially those dealing with structured
output spaces.

In this talk, I will argue that it is often possible to derive a
surrogate supervision signal from a small amount of background
knowledge and often plentiful weakly structured unsupervised data.  We
call this setting "learning with incidental supervision", and study it
in the context of the following tasks.  First, we consider the problem
of Named Entity (NE) annotation transfer to a resource-poor language in
a bilingual corpus.  We demonstrate that temporal similarity of NE
counterparts across languages can be used as an incidental supervision
signal to drive learning of a discriminative transliteration model. 
Second, we consider the task of unsupervised aggregation of structured
output models.  We demonstrate for ranked data that agreement between
constituent models can serve as an incidental supervision signal
sufficient to learn an effective aggregation model.

Bio:

Alexandre Klementiev is a Ph.D.
candidate at the UIUC Department of Computer Science working with Prof.
Dan Roth.  His research interests are on the intersection of Machine
Learning and Natural Language Processing.  More specifically, he is
interested in weakly supervised learning problems in NLP, multilingual
information extraction, and information fusion.