niekrasz.bib
@INPROCEEDINGS{ChaudhriCheyerEtAl06_case,
AUTHOR = {Vinay K. Chaudhri and Adam Cheyer and Richard Guili and Bill Jarrold
and Karen L. Myers and John Niekrasz},
TITLE = {A case study in engineering a knowledge base for an intelligent personal
assistant},
BOOKTITLE = {{Proceedings of the Semantic Desktop and Social Semantic Collaboration
Workshop (SemDesk)}},
LOCATION = {Athens, Georgia},
YEAR = {2006},
ABSTRACT = {We present a case study in engineering a knowledge base to meet the
requirements of an intelligent personal assistant. The assistant
is designed to function as part of a semantic desktop application,
with the goal of helping a user manage and organize his information
as well as supporting the user in performing tasks. We describe the
knowledge base development process, the knowledge engineering challenges
we faced in the process and our solutions to them, and important
lessons learned during the process.},
MONTH = NOV,
PDF = {ChaudhriCheyerEtAl06_case.pdf}
}
@INPROCEEDINGS{ChengBrattEtAl04_Wizard,
AUTHOR = {Hua Cheng and Harry Bratt and Rohit Mishra and Elizabeth Shriberg
and Sandra Upson and Joyce Chen and Fuliang Weng and Stanley Peters
and Lawrence Cavedon and John Niekrasz},
TITLE = {A {W}izard of {O}z framework for collecting spoken human-computer
dialogs},
BOOKTITLE = {{Proceedings of the 8th International Conference on Spoken Language
Processing (INTERSPEECH - ICSLP)}},
LOCATION = {Jeju Island, Korea},
YEAR = {2004},
PAGES = {2269--2272},
ABSTRACT = {This paper describes a data collection process aimed at gathering
human-computer dialogs in high-stress or ''busy'' domains where the
user is concentrating on tasks other than the conversation, for example,
when driving a car. Designing spoken dialog interfaces for such domains
is extremely challenging and the data collected will help us improve
the dialog systeminterface and performance, understand howhumans
performthese tasks with respect to stressful situations, and obtain
speech utterances for extracting prosodic features. This paper describes
the experimental design for collecting speech data in a simulated
driving environment.},
MONTH = OCT,
PDF = {ChengBrattEtAl04_Wizard.pdf}
}
@INPROCEEDINGS{EhlenLaidebeureEtAl06_Browsing,
AUTHOR = {Patrick Ehlen and St\{'e}phane Laidebeure and John Niekrasz and Matthew
Purver and John Dowding and Stanley Peters},
TITLE = {Browsing meetings: {A}utomatic understanding, presentation and feedback
for multi-party conversations},
BOOKTITLE = {{brandial'06: Proceedings of the 10th Workshop on the Semantics and
Pragmatics of Dialogue (SemDial-10)}},
LOCATION = {Potsdam, Germany},
YEAR = {2006},
PAGES = {173--174},
ABSTRACT = {We present a system for extracting useful information from multi-party
meetings and presenting the results to users via a browser. Users
can view automatically extracted discussion topics and action items,
initially seeing high-level descriptions, but with the ability to
click through to meeting audio and video. Users can also add value:
new topics can be defined and searched for, and action items can
be edited or corrected, deleted or confirmed. These feedback actions
are used as implicit supervision by the understanding agents, retraining
classifier models for improved or user-tailored performance.},
MONTH = SEP,
PDF = {EhlenLaidebeureEtAl06_Browsing.pdf}
}
@INPROCEEDINGS{EhlenPurverEtAl07_meeting,
AUTHOR = {Patrick Ehlen and Matthew Purver and John Niekrasz},
TITLE = {A meeting browser that learns},
BOOKTITLE = {{Interaction Challenges for Intelligent Assistants: Papers from the
2007 AAAI Spring Symposium: Technical Report SS-07-04}},
LOCATION = {Stanford, California},
YEAR = {2007},
PAGES = {33-40},
PUBLISHER = {AAAI Press},
ABSTRACT = {We present a system for extracting useful information from multi-party
meetings and presenting the results to users via a browser. Users
can view automatically extracted discussion topics and action items,
initially seeing high-level descriptions, but with the ability to
click through to meeting audio and video. Users can also add value
by defining and searching for new topics and editing, correcting,
deleting, or confirming action items. These feedback actions are
used as implicit supervision by the understanding agents, retraining
classifier models for improved or user-tailored performance.},
ISBN = {978-1-57735-313-3},
MONTH = MAR,
PDF = {EhlenPurverEtAl07_meeting.pdf}
}
@INPROCEEDINGS{EhlenThe07_Multimodal,
AUTHOR = {Patrick Ehlen and {The CALO Team}},
TITLE = {Multimodal Meeting Capture and Understanding with {The CALO Meeting
Assistant}},
BOOKTITLE = {{Proceedings of the 2007 Machine Learning and Multimodal Interaction
Workshop (MLMI)}},
LOCATION = {Brno, Czech Republic},
YEAR = {2007},
NOTE = {Demo.},
ABSTRACT = {The CALO Meeting Assistant is a multimodal meeting assistant technology
that integrates speech, gestures, and multimodal data collected from
multiparty interactions during meetings. Using machine learning and
robust discourse processing, it provides a rich, browsable record
of a meeting.},
MONTH = JUN,
PDF = {EhlenThe07_Multimodal.pdf}
}
@INPROCEEDINGS{GruensteinCavedonEtAl04_Managing,
AUTHOR = {Alexander Gruenstein and Lawrence Cavedon and John Niekrasz and Dominic
Widdows and Stanley Peters},
TITLE = {Managing uncertainty in dialogue information state for real time
understanding of multi-human meeting dialogues},
BOOKTITLE = {{Proceedings of the 8th Workshop on the Semantics and Pragmatics
of Dialogue (SEMDIAL)}},
LOCATION = {Barcelona, Spain},
YEAR = {2004},
PAGES = {152--153},
ABSTRACT = {We are concerned with tracking and understanding dialogue between
multiple human participants specifically, in meetings in such a way
that the dialogue system does not intervene. In this scenario, the
system is not able to provide feedback on whether or not it has understood,
and is unable to ask for clarification or ambiguity resolution. Our
ultimate aim is to model humanhuman dialogue (to the extent that
it is feasible) in real-time, providing useful services (e.g. relevant
document retrieval) and answering queries about the dialogue state
and history (e.g. what action items do we have so far?). Our approach
has been to extend our existing dialogue system, based on the information-state
update approach which supports a rich semantic interpretation of
multi-utterance constructions to cope with the added uncertainty
inherent in two-person meetings in which the participants speak,
point, and draw on a whiteboard.},
ISBN = {84-609-2205-7},
MONTH = JUL,
PDF = {GruensteinCavedonEtAl04_Managing.pdf}
}
@INCOLLECTION{GruensteinNiekraszEtAl07_Meeting,
AUTHOR = {Alexander Gruenstein and John Niekrasz and Matthew Purver},
TITLE = {Meeting Structure Annotation: Annotations Collected with a General
Purpose Toolkit},
BOOKTITLE = {{Recent Trends in Discourse and Dialogue}},
PUBLISHER = {Springer-Verlag},
YEAR = {2007},
EDITOR = {L. Dybkjaer and W. Minker},
SERIES = {Text, Speech and Language Technology series},
ABSTRACT = {We describe a generic set of tools for representing, annotating, and
analyzing multi-party discourse, including: an ontology of multimodal
discourse, a programming interface for that ontology, and NOMOS -
a flexible and extensible toolkit for browsing and annotating discourse.
We describe applications built using the NOMOS framework to facilitate
a real annotation task, as well as for visualizing and adjusting
features for machine learning tasks. We then present a set of of
hierarchical topic segmentations and action item subdialogues collected
over 56 meetings from the ICSI and ISL meeting corpora using our
tools. These annotations are designed to support research towards
automatic meeting understanding.},
PDF = {GruensteinNiekraszEtAl07_Meeting.pdf}
}
@INPROCEEDINGS{GruensteinNiekraszEtAl05_Meeting,
AUTHOR = {Alexander Gruenstein and John Niekrasz and Matthew Purver},
TITLE = {Meeting structure annotation: {D}ata and tools},
BOOKTITLE = {{Proceedings of the 6th SIGdial Workshop on Discourse and Dialogue}},
LOCATION = {Lisbon, Portugal},
YEAR = {2005},
PAGES = {117--127},
ABSTRACT = {We present a set of annotations of hierarchical topic segmentations
and action item subdialogues collected over 65 meetings from the
ICSI and ISL meeting corpora, designed to support automatic meeting
understanding and analysis. We describe an architecture for representing,
annotating, and analyzing multi-party discourse, including: an ontology
of multimodal discourse, a programming interface for that ontology,
and an audiovisual toolkit which facilitates browsing and annotating
discourse, as well as visualizing and adjusting features for machine
learning tasks.},
INSTITUTION = {Association for Computational Linguistics},
MONTH = SEP,
PDF = {GruensteinNiekraszEtAl05_Meeting.pdf}
}
@INPROCEEDINGS{GuptaNiekraszEtAl07_Resolving,
AUTHOR = {Surabhi Gupta and John Niekrasz and Matthew Purver and Dan Jurafsky},
TITLE = {Resolving ''You'' in Multi-Party Dialog},
BOOKTITLE = {{Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue}},
LOCATION = {Antwerp, Belgium},
YEAR = {2007},
ABSTRACT = {This paper presents experiments into the resolution of ''you'' in
multi-party dialog, dividing this process into three tasks: distinguishing
between generic and referential uses; distinguishing between singular
and plural reference; and identifying the referred-to addressee(s).
First we perform a multi-corpus experiment into referentiality detection,
achieving an accuracy of 73.8\% on multi-party data. Our next experiment
deals with singular vs. plural reference, achieving an accuracy of
71.4\%. Our last experiment is on the task of addressee identification
for referential ''you'' utterances, achieving an accuracy of 67\%
without the use of visual information; the output of the first two
experiments is shown to help.},
INSTITUTION = {Association for Computational Linguistics},
MONTH = SEP,
PDF = {GuptaNiekraszEtAl07_Resolving.pdf}
}
@INPROCEEDINGS{KaiserDemirdjianEtAl04_multimodal,
AUTHOR = {Ed Kaiser and David Demirdjian and Alexander Gruenstein and Xiaoguang
Li and John Niekrasz and Matt Wesson and Sanjeev Kumar},
TITLE = {A multimodal learning interface for sketch, speak and point creation
of a schedule chart},
BOOKTITLE = {{Proceedings of the 6th International Conference on Multimodal Interfaces
(ICMI)}},
LOCATION = {State College, Pennsylvania},
YEAR = {2004},
PAGES = {329--330},
PUBLISHER = {{ACM Press}},
ABSTRACT = {We present a video demonstration of an agent-based test bed application
for ongoing research into multi-user, multimodal, computer-assisted
meetings. The system tracks a two person scheduling meeting: one
person standing at a touch sensitive whiteboard creating a Gantt
chart, while another person looks on in view of a calibrated stereo
camera. The stereo camera performs real-time, untethered, vision-based
tracking of the onlooker's head, torso and limb movements, which
in turn are routed to a 3D-gesture recognition agent. Using speech,
3D deictic gesture and 2D object de-referencing the system is able
to track the onlooker's suggestion to move a specific milestone.
The system also has a speech recognition agent capable of recognizing
out-of-vocabulary (OOV) words as phonetic sequences. Thus when a
user at the whiteboard speaks an OOV label name for a chart constituent
while also writing it, the OOV speech is combined with letter sequences
hypothesized by the handwriting recognizer to yield an orthography,
pronunciation and semantics for the new label. These are then learned
dynamically by the system and become immediately available for future
recognition.},
ISBN = {1-58113-995-0},
MONTH = OCT,
PDF = {KaiserDemirdjianEtAl04_multimodal.pdf}
}
@INPROCEEDINGS{NiekraszGruenstein06_NOMOS,
AUTHOR = {John Niekrasz and Alexander Gruenstein},
TITLE = {{NOMOS}: {A} {S}emantic {W}eb software framework for annotation of
multimodal corpora},
BOOKTITLE = {{Proceedings of the 5th International Conference on Language Resources
and Evaluation (LREC)}},
LOCATION = {Genoa, Italy},
YEAR = {2006},
ABSTRACT = {We present NOMOS, an open-source software framework for annotation,
processing, and analysis of multimodal corpora. NOMOS is designed
for use by annotators, corpus developers, and corpus consumers, emphasizing
configurability for a variety of specific annotation tasks. Its features
include synchronized multi-channel audio and video playback, compatibility
with several corpora, platform independence, and mixed display of
temporal, non-temporal, and relational information. We describe NOMOS
from two perspectives. First, we present its software architecture,
highlighting its principal difference from comparable systems: its
use of an OWL-based semantic annotation back-end which provides automatic
inference capabilities and a well-defined method for layering datasets.
Second, we describe how the system is used. For corpus development
and annotation we present a typical use scenario involving the creation
of a schema and specialization of the user interface. For processing
and analysis we describe the GUI- and Java-based methods available,
including a GUI for query construction and execution, and an automatically
generated schema-conforming Java API for processing of annotations.
Additionally, we present some specific annotation and research tasks
for which NOMOS has been specialized and used, including topic segmentation
and decision-point annotation of meetings.},
MONTH = MAY,
PDF = {NiekraszGruenstein06_NOMOS.pdf}
}
@INPROCEEDINGS{NiekraszGruensteinEtAl04_Multi-human,
AUTHOR = {John Niekrasz and Alexander Gruenstein and Lawrence Cavedon},
TITLE = {Multi-human dialogue understanding for assisting artifact-producing
meetings},
BOOKTITLE = {{Proceedings of the 20th International Conference on Computational
Linguistics (COLING)}},
LOCATION = {Geneva, Switzerland},
YEAR = {2004},
PAGES = {432--438},
ABSTRACT = {In this paper we present the dialogue understanding components of
an architecture for assisting multi-human conversations in artifact-producing
meetings: meetings in which tangible products such as project planning
charts are created. Novel aspects of our system include multimodal
ambiguity resolution, modular ontology-driven artifact manipulation,
and a meeting browser for use during and after meetings. We describe
the software architecture and demonstrate the system using an example
multimodal dialogue.},
MONTH = AUG,
PDF = {NiekraszGruensteinEtAl04_Multi-human.pdf}
}
@INCOLLECTION{NiekraszPurver06_multimodal,
AUTHOR = {John Niekrasz and Matthew Purver},
TITLE = {A multimodal discourse ontology for meeting understanding},
BOOKTITLE = {{Machine Learning for Multimodal Interaction: Second International
Workshop, MLMI 2005, Edinburgh, UK, July 11--13, 2005, Revised Selected
Papers}},
PUBLISHER = {Springer},
YEAR = {2006},
EDITOR = {Steve Renals and Samy Bengio},
VOLUME = {3869},
SERIES = {Lecture Notes in Computer Science},
PAGES = {162--173},
ABSTRACT = {In this paper, we present a multimodal discourse ontology that serves
as a knowledge representation and annotation framework for the discourse
understanding component of an artificial personal office assistant.
The ontology models components of natural language, multimodal communication,
multi-party dialogue structure, meeting structure, and the physical
and temporal aspects of human communication. We compare our models
to those from the research literature and from similar applications.
We also highlight some annotations which have been made in conformance
with the ontology as well as some algorithms which have been trained
on these data and suggest elements of the ontology that may be of
immediate interest for further annotation by human or automated means.},
PDF = {NiekraszPurver06_multimodal.pdf}
}
@INPROCEEDINGS{NiekraszPurverEtAl05_Ontology-based,
AUTHOR = {John Niekrasz and Matthew Purver and John Dowding and Stanley Peters},
TITLE = {Ontology-based discourse understanding for a persistent meeting assistant},
BOOKTITLE = {{Persistent Assistants: Living and Working with AI: Papers from the
2005 AAAI Spring Symposium: Technical Report SS-05-05}},
LOCATION = {Stanford, California},
YEAR = {2005},
PAGES = {26--33},
PUBLISHER = {AAAI Press},
ABSTRACT = {In this paper, we present research toward ontology-based understanding
of discourse in meetings and describe an ontology of multimodal discourse
designed for this purpose. We investigate its application in an integrated
but modular architecture which uses semantically annotated knowledge
of communicative meeting activity as well as discourse subject matter.
We highlight how this approach assists in improving system performance
over time and supports understanding in a changing and persistent
environment. We also describe current and future plans for ontology-driven
robust natural language understanding in the presence of the highly
ambiguous and errorful input typical of the meeting domain.},
ISBN = {1-57735-231-9},
MONTH = MAR,
PDF = {NiekraszPurverEtAl05_Ontology-based.pdf}
}
@INPROCEEDINGS{PallottaNiekraszEtAl05_Collaborative,
AUTHOR = {Vincenzo Pallotta and John Niekrasz and Matthew Purver},
TITLE = {Collaborative and argumentative models of natural discussions},
BOOKTITLE = {{Proceedings of the 5th Workshop on Computational Models of Natural
Argument (CMNA)}},
LOCATION = {Edinburgh, Scotland},
YEAR = {2005},
ABSTRACT = {We report in this paper experiences and insights resulting from the
first two years of work in two similar projects on meeting tracking
and understanding. The projects are the DARPA-funded CALO project
and the Swiss National research project IM2. The findings from these
two projects have been shared and compared in order to come up with
a joint ontology as a model for argumentative discussions in meetings.
We highlight the complexity of the problem in modeling interaction
and discourse in argumentative discussions and we propose a solution
based on the construction of a specific knowledge base.},
KEYWORDS = {Argumentation},
MONTH = JUL,
PDF = {PallottaNiekraszEtAl05_Collaborative.pdf}
}
@INPROCEEDINGS{PurverDowdingEtAl07_Detecting,
AUTHOR = {Matthew Purver and John Dowding and John Niekrasz and Patrick Ehlen
and Sharareh Noorbaloochi},
TITLE = {Detecting and Summarizing Action Items in Multi-Party Dialogue},
BOOKTITLE = {{Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue}},
LOCATION = {Antwerp, Belgium},
YEAR = {2007},
ABSTRACT = {This paper addresses the problem of identifying action items discussed
in open-domain conversational speech, and does so in two stages:
firstly, detecting the subdialogues in which action items are proposed,
discussed and committed to; and secondly, extracting the phrases
that accurately capture or summarize the tasks they involve. While
the detection problem is hard, we show that by taking account of
dialogue structure we can achieve reasonable accuracy. We then describe
a semantic parser that identifies potential summarizing phrases,
and show that for some task properties these can be more informative
than plain utterance transcriptions.},
INSTITUTION = {Association for Computational Linguistics},
MONTH = SEP,
PDF = {PurverDowdingEtAl07_Detecting.pdf}
}
@INCOLLECTION{PurverEhlenEtAl06_Detecting,
AUTHOR = {Matthew Purver and Patrick Ehlen and John Niekrasz},
TITLE = {Detecting action items in multi-party meetings: {A}nnotation and
initial experiments},
BOOKTITLE = {{Machine Learning for Multimodal Interaction: Third International
Workshop, MLMI 2006, Bethesda, MD, USA, May 1--4, 2006, Revised Selected
Papers}},
PUBLISHER = {Springer},
YEAR = {2006},
EDITOR = {Steve Renals and Samy Bengio and Jonathan Fiscus},
VOLUME = {4299},
SERIES = {Lecture Notes in Computer Science},
PAGES = {200--211},
ABSTRACT = {This paper presents the results of initial investigation and experiments
into automatic action item detection from transcripts of multi-party
human-human meetings. We start from our previous flat action item
annotations, and show that automatic classification performance is
limited. We then describe a new hierarchical annotation schema based
on the roles utterances play in the action item assignment process,
and propose a corresponding approach to automatic detection that
promises improved classification accuracy while also enabling the
extraction of useful information for summarization and reporting.},
KEYWORDS = {Action Items},
PDF = {PurverEhlenEtAl06_Detecting.pdf}
}
@INPROCEEDINGS{PurverEhlenEtAl06_Shallow,
AUTHOR = {Matthew Purver and Patrick Ehlen and John Niekrasz},
TITLE = {Shallow discourse structure for action item detection},
BOOKTITLE = {{Proceedings of the 2006 HLT-NAACL Workshop on Analyzing Conversations
in Text and Speech}},
LOCATION = {New York City, New York},
YEAR = {2006},
PAGES = {31--34},
ABSTRACT = {We investigated automatic action item detection from transcripts of
multi-party meetings. Unlike previous work (Gruenstein et al., 2005),
we use a new hierarchical annotation scheme based on the roles utterances
play in the action item assignment process, and propose an approach
to automatic detection that promises improved classification accuracy
while enabling the extraction of useful information for summarization
and reporting.},
KEYWORDS = {Action Items},
MONTH = JUN,
PDF = {PurverEhlenEtAl06_Shallow.pdf}
}
@INCOLLECTION{PurverNiekraszEtAl07_Automatic,
AUTHOR = {Matthew Purver and John Niekrasz and Patrick Ehlen},
TITLE = {Automatic annotation of dialogue structure from simple user interaction},
BOOKTITLE = {{Machine Learning for Multimodal Interaction: Fourth International
Workshop, MLMI 2007, Brno, Czech Republic, Revised Selected Papers}},
PUBLISHER = {Springer-Verlag},
YEAR = {2007},
EDITOR = {Andrei Popescu-Belis and Steve Renals and Herve Bourlard},
VOLUME = {4892},
SERIES = {Lecture Notes in Computer Science},
PAGES = {44--59},
ABSTRACT = {Previously, we presented a method for automatic detection of action
items from natural conversation. This method relies on supervised
classification techniques that are trained on data annotated according
to a hierarchical notion of dialogue structure; data which are expensive
and time-consuming to produce. Subsequently, we presented a meeting
browser which allows users to view a set of automatically-produced
action item summaries and give feedback on their accuracy. In this
paper, we investigate methods of using this kind of feedback as implicit
supervision, in order to bypass the costly annotation process and
enable machine learning through use. We investigate, through the
transformation of human annotations into hypothetical idealized user
interactions, the relative utility of various modes of user interaction
as well as various techniques for automatically producing training
instances from interaction. We show that performance improvements
are possible from interaction alone, even with interfaces that present
very low cognitive load to users.},
LOCATION = {Brno, Czech Republic},
PDF = {PurverNiekraszEtAl07_Automatic.pdf}
}
@INPROCEEDINGS{PurverNiekraszEtAl05_Ontology-based,
AUTHOR = {Matthew Purver and John Niekrasz and Stanley Peters},
TITLE = {Ontology-based multi-party meeting understanding},
BOOKTITLE = {{Proceedings of the 2005 CHI Workshop on The Virtuality Continuum
Revisited}},
LOCATION = {Portland, Oregon},
YEAR = {2005},
ABSTRACT = {This paper describes current and planned research efforts towards
developing multimodal discourse understanding for an automated personal
office assistant. The research is undertaken as part of a project
called The Cognitive Agent that Learns and Organizes (CALO) (see
http://www.ai.sri.com/project/CALO). The CALO assistant is intended
to aid users both personally and as a group in performing office-related
tasks such as coordinating schedules, providing relevant information
for completing tasks, making a record of meetings, and assisting
in fulfilling decisions.},
MONTH = APR,
PDF = {PurverNiekraszEtAl05_Ontology-based.pdf}
}
@INPROCEEDINGS{VossEhlenEtAl07_CALO,
AUTHOR = {Lynn Voss and Patrick Ehlen and {The DARPA CALO Meeting Assistant
Project Team}},
TITLE = {{The CALO Meeting Assistant}},
BOOKTITLE = {{Proceedings of the 2007 Annual Conference of the North American
Chapter of the Association for Computational Linguistics (NAACL-HLT)}},
YEAR = {2007},
NOTE = {Demo.},
ABSTRACT = {The CALO Meeting Assistant is an integrated, multimodal meeting assistant
technology that captures speech, gestures, and multimodal data from
multiparty interactions during meetings, and uses machine learning
and robust discourse processing to provide a rich, browsable record
of a meeting.},
MONTH = APR,
PDF = {VossEhlenEtAl07_CALO.pdf}
}
@COMMENT{{jabref-meta:pdfDirectory:/home/s0093444/data/my-pubs/published-pdfs}}
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