[Colloquium] REMINDER: 3/7 Talks at TTIC: Sameer Singh, University of Washington

[Mary Marre]

When:     Monday, March 7th at 11:00 am

Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526

Who:      Sameer Singh, University of Washington


Title:      Interactive Machine Learning for Information Extraction

Abstract: Most of the world's knowledge, be it factual news, scholarly
research, social communication, subjective opinions, or even fictional
content, is now easily accessible as digitized text. Unfortunately, due to
the unstructured nature of text, much of the useful content in these
documents is hidden. The goal of “information extraction” is to address
this problem: extracting meaningful, structured knowledge (such as graphs
and databases) from text collections. The biggest challenges when using
machine learning for information extraction include the high cost of
obtaining annotated data and lack of guidance on how to understand and fix
mistakes.

In this talk, I propose interpretable representations that allow users and
machine learning models to interact with each other: enabling users to
inject domain knowledge into machine learning and machine learning models
to provided explanations as to why a specific prediction was made. I study
these techniques using relation extraction as the application, an important
subtask of information extraction where the goal is to identify the types
of relations between entities that are expressed in text. I first describe
how symbolic domain knowledge, if provided by the user as first-order logic
statements, can be injected into relational embeddings to improve the
predictions. In the second part of the talk, I present an approach to
“explain” machine learning predictions using symbolic representations,
which the user may annotate for more effective supervision. I present
experiments to demonstrate that an interactive interface between a user and
machine learning is effective in reducing annotation effort and in quickly
training accurate extraction systems.

*Bio*

Sameer Singh is a Postdoctoral Research Associate at the University of
Washington, working on large-scale and interactive machine learning applied
to information extraction and natural language processing. He received his
PhD from the University of Massachusetts, Amherst, during which he also
interned at Microsoft Research, Google Research, and Yahoo! Labs on
massive-scale machine learning. He was recently selected as a DARPA Riser,
won the grand prize in the Yelp dataset challenge, has been awarded the
Yahoo! Key Scientific Challenges and the UMass Graduate School fellowships,
and was a finalist for the Facebook PhD fellowship. (http://sameersingh.org)



Host: Kevin Gimpel, kgimpel at ttic.edu


Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*

On Sun, Mar 6, 2016 at 5:45 PM, Mary Marre <mmarre at ttic.edu> wrote:

> When:     Monday, March 7th at 11:00 am
>
> Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526
>
> Who:      Sameer Singh, University of Washington
>
>
> Title:      Interactive Machine Learning for Information Extraction
>
> Abstract: Most of the world's knowledge, be it factual news, scholarly
> research, social communication, subjective opinions, or even fictional
> content, is now easily accessible as digitized text. Unfortunately, due to
> the unstructured nature of text, much of the useful content in these
> documents is hidden. The goal of “information extraction” is to address
> this problem: extracting meaningful, structured knowledge (such as graphs
> and databases) from text collections. The biggest challenges when using
> machine learning for information extraction include the high cost of
> obtaining annotated data and lack of guidance on how to understand and fix
> mistakes.
>
> In this talk, I propose interpretable representations that allow users and
> machine learning models to interact with each other: enabling users to
> inject domain knowledge into machine learning and machine learning models
> to provided explanations as to why a specific prediction was made. I study
> these techniques using relation extraction as the application, an important
> subtask of information extraction where the goal is to identify the types
> of relations between entities that are expressed in text. I first describe
> how symbolic domain knowledge, if provided by the user as first-order logic
> statements, can be injected into relational embeddings to improve the
> predictions. In the second part of the talk, I present an approach to
> “explain” machine learning predictions using symbolic representations,
> which the user may annotate for more effective supervision. I present
> experiments to demonstrate that an interactive interface between a user and
> machine learning is effective in reducing annotation effort and in quickly
> training accurate extraction systems.
>
> *Bio*
>
> Sameer Singh is a Postdoctoral Research Associate at the University of
> Washington, working on large-scale and interactive machine learning applied
> to information extraction and natural language processing. He received his
> PhD from the University of Massachusetts, Amherst, during which he also
> interned at Microsoft Research, Google Research, and Yahoo! Labs on
> massive-scale machine learning. He was recently selected as a DARPA Riser,
> won the grand prize in the Yelp dataset challenge, has been awarded the
> Yahoo! Key Scientific Challenges and the UMass Graduate School fellowships,
> and was a finalist for the Facebook PhD fellowship. (
> http://sameersingh.org)
>
>
>
> Host: Kevin Gimpel, kgimpel at ttic.edu
>
>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 504*
> *Chicago, IL  60637*
> *p:(773) 834-1757 <%28773%29%20834-1757>*
> *f: (773) 357-6970 <%28773%29%20357-6970>*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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