Overview
I am a 5th year Ph.d. student in the Computer Science Department at the University of Washington.
My graduate adviser is Oren Etzioni; I have collaborated with Doug Downey, Mausam, and Stephen Soderland at UW, in addition to Sumit Basu, Colin Cherry and Bill Dolan during internships at Microsoft Research.
Research Summary
Broadly I am interested in getting computers to better
understand natural language, and new applications this will enable.
I enjoy working with large quantities of text which is not restricted to a narrow domain, such as that found on the
Web or in
Social Media.
Specifically my work focuses on
Information Extraction,
Computational Lexical Semantics,
Latent Variable Models,
Language Processing in Social Media and
Intelligent Interfaces.
Following are short descriptions of specific research directions.
Information Extraction in Social Media
Status Messages written by users of Social Media websites (e.g. Facebook and Twitter)
contain a great deal of timely and important information, however there are also many irrelevant and redundant messages
which can easily lead to Information Overload. No person can read each of
the hundreds of millions of messages produced every day, motivating
the need for systems which can automatically extract
and aggregate information from these dynamically changing text streams.
Off-the shelf tools such as Part of Speech Taggers and Named Entity Recognizers perform poorly when applied to Social Media text due to it's noisy and
unique style. To address this I have been working towards building a set of Twitter-specific text processing tools [EMNLP 2011].
Users of Social Media sites frequently discuss events which will
occur in the future.
By annotating Named Entities and resolving temporal expressions (for example "next Friday"), we are able to automatically
extract a
calendar of popular events occurring in the near future from Twitter.
Conversational Modeling in Social Media
In addition to discussing upcoming events, users of social networking sites are having public conversations at an unprecedented scale. This presents a unique opportunity
to collect millions of naturally occuring conversations and investigate new data-driven techniques for conversational modeling.
I have worked on unsupervised modeling of dialogue acts in Twitter [NAACL 2010]. By remaining agnostic about the set of classes, we are
able to learn a model which provides insight into the nature of communication in a new medium.
I have investigated the feasibility of automatically replying to status messages by adapting techniques
from Statistical Machine Translation [EMNLP 2011], using millions of naturally occurring Twitter conversations
as parallel text.
Although there are many differences between conversation and translation, with a few conversation-specific adaptations we
are able to build Response Models which often generate appropriate replies to Twitter status posts.
This work has several possible applications, including conversationally aware predictive text entry.
A recent talk on this work can be viewed here.
Latent Variable Models of Lexical Semantics
I have applied a variant of
Latent Dirichlet Allocation to automatically infer the argument types or
Selectional Preferences of
textual relations
[ACL 2010]. Generative models have the advantage that they provide a principled way to perform many different
kinds of probabilistic queries about the data. For example, our model of selectional preferences is useful in filtering improper applications of inference rules in context, showing a substantial improvement over a state-of-the-art rule-filtering system which makes use of a predefined set of classes.
The topics discovered by our model can be browsed
here. Inference and evaluation code is available for download
here.
In addition, I have investigated Distant Supervision with Topic Models. As a distant source of supervision we make use of facts from Freebase, a large, open-domain database, to generate constraints in the topic model.
This approach leverages the ambiguous training data provided by Freebase in a principled way, significantly
outperforming Co-Training on a weakly supervised named entity classification task [EMNLP 2011].
Utilizing Implicit Feedback in Interactive File Selection
Selection tasks are common in modern computer interfaces; we are often required to
select multiple files, emails, and other data entries for copying, modification, deletion etc...
Complex selection tasks can require many clicks and mouse movements on behalf of the user;
to aid users with these complex selections we propose an interactive machine learning solution
[IUI 2009].
In addition to making use of explicit selections and deselections, we utilize implicit
features of the user's behaviour such as passing over files, or proximity in the interface.
Since the behaviour features are task-independent, we use historical interaction traces as training
data. A video demonstration of our file-selection prototype can be viewed
here.
Finding Contradictions in Web Text
Many textual relations map one argument to a unique value. For example the verb
assassinated should map each direct object to a unique subject. We investigate automatically
classifying relation functionality using an unsupervised EM-style algorithm, and evaluate performance at discovering naturally occurring contradictions within a large web corpus
[EMNLP 2008]. We show
that contradiction detection on the web is a difficult task for a variety of reasons including name ambiguity (e.g. John Smith was born in many different locations), synonyms and meronyms
(Mozart was born in both Salzburg and Austria).
Interactive Information Integration with HTML Tables & Freebase
As part of the grad databases class, I investigated applying data-integration techniques to augment HTML tables with additional data from Freebase, in addition to enabling users to quickly verify and contribute data contained in the table. Users can choose to display columns which are not present in the original table, but for which data exists in Freebase, providing direct benefit. They can also easily verify our algorithmically generated mapping from table columns to Freebase attributes, alowing data contained in the table (but missing in Freebase) to be imported. Details on this project including a prototype Firefox browser plugin can be found
here.
Teaching Experience
I have served as teaching assistant for the Machine Learning class at UW in
Winter 2010 and
Winter 2011, where I helped design homework assignments, answered student questions one on one, and presented an hour long lecture in class.
Also at UW, I mentored Sam Clark during his senior year and Master's program; during this time I supervised his work annotating a corpus of Tweets with Parts of Speech,
and building a Part-of-Speech Tagger for Twitter [EMNLP 2011]. Sam is now at Decide.com.
Publications
2011
Named Entity Recognition in Tweets: An Experimental Study
Alan Ritter, Sam Clark, Mausam, Oren Etzioni
Proceedings of EMNLP 2011
Slides
Data-Driven Response Generation in Social Media
Alan Ritter, Colin Cherry, Bill Dolan
Proceedings of EMNLP 2011
Slides
2010
A Latent Dirichlet Allocation Method for Selectional Preferences
Alan Ritter, Mausam, Oren Etzioni
Proceedings of ACL 2010
Slides
Unsupervised Modeling of Twitter Conversations
Alan Ritter, Colin Cherry, Bill Dolan
Proceedings of HLT-NAACL 2010
Slides
2009
Learning to Generalize for Complex Selection Tasks
Alan Ritter and Sumit Basu
Best Student Paper Award
IUI 2009
Video
Slides
Filter, Rank, and Transfer the Knowledge: Learning to Chat
Sina Jafarpour and Chris Burges, Alan Ritter
NIPS Workshop on Advances in Ranking, Vancouver, Canada, 2009
What Is This, Anyway: Automatic Hypernym Discovery
Alan Ritter, Stephen Soderland, and Oren Etzioni
2009 AAAI Spring Symposium on Learning by Reading and Learning to Read
2008
It's a Contradiction -- No, It's Not: A Case Study using Functional Relations
Alan Ritter, Doug Downey, Stephen Soderland, and Oren Etzioni
EMNLP 2008
Slides
2006
Distributional Word Clustering in Parallel
Alan Ritter, James Hearne, Philip Nelson
ISCA PDCS 2006
Machine Learning Approach to Tuning Distributed Operating System Load Balancing Algorithms
Michael Meehan, Alan Ritter
ISCA PDCS 2006
NDIS Network Driver
Alan Ritter
Dr. Dobb's Journal, January 2006
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Software/Data/Demos
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Presentations
NRL Seminar
openMosix Load Balancing Poster