Loading...

Ajay Nagesh: Exploring Relational Features and Learning

188 views

Loading...

Loading...

Transcript

The interactive transcript could not be loaded.

Loading...

Loading...

Rating is available when the video has been rented.
This feature is not available right now. Please try again later.
Published on Oct 6, 2016

Ajay Nagesh

Exploring Relational Features and Learning under Distant Supervision for Information Extraction Tasks

Abstract:
Information Extraction has become an indispensable tool in our quest to handle the data deluge of the information age. In this talk, we discuss the categorization of complex relational features and outline methods to learn feature combinations through induction. We demonstrate the efficacy of induction techniques in learning rules for the identification of named entities in text – the novelty being the application of induction techniques to learn in a very expressive declarative rule language. Next, we discuss our investigations in the paradigm of distant supervision, which facilitates the creation of large albeit noisy training data. We devise an inference framework in which constraints can be easily specified in learning relation extractors. We reformulate the learning objective in a max-margin framework. To the best of our knowledge, our formulation is the first to optimize multi-variate non-linear performance measures such as F1 for a latent variable structure prediction task. Towards the end, we will briefly touch upon some recent exploratory work to leverage matrix completion methods and novel embedding techniques for predicting a richer fine-grained set of entity types to help in downstream applications such as Relation Extraction and Question Answering.

Loading...

When autoplay is enabled, a suggested video will automatically play next.

Up next


to add this to Watch Later

Add to

Loading playlists...