James Allen

Rochester

Also published as: James F. Allen

Other people with similar names: James Allan (UMass Amherst)


2020

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A Broad-Coverage Deep Semantic Lexicon for Verbs
James Allen | Hannah An | Ritwik Bose | Will de Beaumont | Choh Man Teng
Proceedings of the 12th Language Resources and Evaluation Conference

Progress on deep language understanding is inhibited by the lack of a broad coverage lexicon that connects linguistic behavior to ontological concepts and axioms. We have developed COLLIE-V, a deep lexical resource for verbs, with the coverage of WordNet and syntactic and semantic details that meet or exceed existing resources. Bootstrapping from a hand-built lexicon and ontology, new ontological concepts and lexical entries, together with semantic role preferences and entailment axioms, are automatically derived by combining multiple constraints from parsing dictionary definitions and examples. We evaluated the accuracy of the technique along a number of different dimensions and were able to obtain high accuracy in deriving new concepts and lexical entries. COLLIE-V is publicly available.

2018

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A Notion of Semantic Coherence for Underspecified Semantic Representation
Mehdi Manshadi | Daniel Gildea | James F. Allen
Computational Linguistics, Volume 44, Issue 1 - April 2018

The general problem of finding satisfying solutions to constraint-based underspecified representations of quantifier scope is NP-complete. Existing frameworks, including Dominance Graphs, Minimal Recursion Semantics, and Hole Semantics, have struggled to balance expressivity and tractability in order to cover real natural language sentences with efficient algorithms. We address this trade-off with a general principle of coherence, which requires that every variable introduced in the domain of discourse must contribute to the overall semantics of the sentence. We show that every underspecified representation meeting this criterion can be efficiently processed, and that our set of representations subsumes all previously identified tractable sets.

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Tackling the Story Ending Biases in The Story Cloze Test
Rishi Sharma | James Allen | Omid Bakhshandeh | Nasrin Mostafazadeh
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

The Story Cloze Test (SCT) is a recent framework for evaluating story comprehension and script learning. There have been a variety of models tackling the SCT so far. Although the original goal behind the SCT was to require systems to perform deep language understanding and commonsense reasoning for successful narrative understanding, some recent models could perform significantly better than the initial baselines by leveraging human-authorship biases discovered in the SCT dataset. In order to shed some light on this issue, we have performed various data analysis and analyzed a variety of top performing models presented for this task. Given the statistics we have aggregated, we have designed a new crowdsourcing scheme that creates a new SCT dataset, which overcomes some of the biases. We benchmark a few models on the new dataset and show that the top-performing model on the original SCT dataset fails to keep up its performance. Our findings further signify the importance of benchmarking NLP systems on various evolving test sets.

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Building and Learning Structures in a Situated Blocks World Through Deep Language Understanding
Ian Perera | James Allen | Choh Man Teng | Lucian Galescu
Proceedings of the First International Workshop on Spatial Language Understanding

We demonstrate a system for understanding natural language utterances for structure description and placement in a situated blocks world context. By relying on a rich, domain-specific adaptation of a generic ontology and a logical form structure produced by a semantic parser, we obviate the need for an intermediate, domain-specific representation and can produce a reasoner that grounds and reasons over concepts and constraints with real-valued data. This linguistic base enables more flexibility in interpreting natural language expressions invoking intrinsic concepts and features of structures and space. We demonstrate some of the capabilities of a system grounded in deep language understanding and present initial results in a structure learning task.

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A Situated Dialogue System for Learning Structural Concepts in Blocks World
Ian Perera | James Allen | Choh Man Teng | Lucian Galescu
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

We present a modular, end-to-end dialogue system for a situated agent to address a multimodal, natural language dialogue task in which the agent learns complex representations of block structure classes through assertions, demonstrations, and questioning. The concept to learn is provided to the user through a set of positive and negative visual examples, from which the user determines the underlying constraints to be provided to the system in natural language. The system in turn asks questions about demonstrated examples and simulates new examples to check its knowledge and verify the user’s description is complete. We find that this task is non-trivial for users and generates natural language that is varied yet understood by our deep language understanding architecture.

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Cogent: A Generic Dialogue System Shell Based on a Collaborative Problem Solving Model
Lucian Galescu | Choh Man Teng | James Allen | Ian Perera
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

The bulk of current research in dialogue systems is focused on fairly simple task models, primarily state-based. Progress on developing dialogue systems for more complex tasks has been limited by the lack generic toolkits to build from. In this paper we report on our development from the ground up of a new dialogue model based on collaborative problem solving. We implemented the model in a dialogue system shell (Cogent) that al-lows developers to plug in problem-solving agents to create dialogue systems in new domains. The Cogent shell has now been used by several independent teams of researchers to develop dialogue systems in different domains, with varied lexicons and interaction style, each with their own problem-solving back-end. We believe this to be the first practical demonstration of the feasibility of a CPS-based dialogue system shell.

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Putting Semantics into Semantic Roles
James Allen | Choh Man Teng
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

While there have been many proposals for theories of semantic roles over the years, these models are mostly justified by intuition and the only evaluation methods have been inter-annotator agreement. We explore three different ideas for providing more rigorous theories of semantic roles. These ideas give rise to more objective criteria for designing role sets, and lend themselves to some experimental evaluation. We illustrate the discussion by examining the semantic roles in TRIPS.

2017

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LSDSem 2017 Shared Task: The Story Cloze Test
Nasrin Mostafazadeh | Michael Roth | Annie Louis | Nathanael Chambers | James Allen
Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics

The LSDSem’17 shared task is the Story Cloze Test, a new evaluation for story understanding and script learning. This test provides a system with a four-sentence story and two possible endings, and the system must choose the correct ending to the story. Successful narrative understanding (getting closer to human performance of 100%) requires systems to link various levels of semantics to commonsense knowledge. A total of eight systems participated in the shared task, with a variety of approaches including.

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Compositionality in Verb-Particle Constructions
Archna Bhatia | Choh Man Teng | James Allen
Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)

We are developing a broad-coverage deep semantic lexicon for a system that parses sentences into a logical form expressed in a rich ontology that supports reasoning. In this paper we look at verb-particle constructions (VPCs), and the extent to which they can be treated compositionally vs idiomatically. First we distinguish between the different types of VPCs based on their compositionality and then present a set of heuristics for classifying specific instances as compositional or not. We then identify a small set of general sense classes for particles when used compositionally and discuss the resulting lexical representations that are being added to the lexicon. By treating VPCs as compositional whenever possible, we attain broad coverage in a compact way, and also enable interpretations of novel VPC usages not explicitly present in the lexicon.

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Apples to Apples: Learning Semantics of Common Entities Through a Novel Comprehension Task
Omid Bakhshandeh | James Allen
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Understanding common entities and their attributes is a primary requirement for any system that comprehends natural language. In order to enable learning about common entities, we introduce a novel machine comprehension task, GuessTwo: given a short paragraph comparing different aspects of two real-world semantically-similar entities, a system should guess what those entities are. Accomplishing this task requires deep language understanding which enables inference, connecting each comparison paragraph to different levels of knowledge about world entities and their attributes. So far we have crowdsourced a dataset of more than 14K comparison paragraphs comparing entities from a variety of categories such as fruits and animals. We have designed two schemes for evaluation: open-ended, and binary-choice prediction. For benchmarking further progress in the task, we have collected a set of paragraphs as the test set on which human can accomplish the task with an accuracy of 94.2% on open-ended prediction. We have implemented various models for tackling the task, ranging from semantic-driven to neural models. The semantic-driven approach outperforms the neural models, however, the results indicate that the task is very challenging across the models.

2016

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CaTeRS: Causal and Temporal Relation Scheme for Semantic Annotation of Event Structures
Nasrin Mostafazadeh | Alyson Grealish | Nathanael Chambers | James Allen | Lucy Vanderwende
Proceedings of the Fourth Workshop on Events

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Story Cloze Evaluator: Vector Space Representation Evaluation by Predicting What Happens Next
Nasrin Mostafazadeh | Lucy Vanderwende | Wen-tau Yih | Pushmeet Kohli | James Allen
Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP

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Towards Broad-coverage Meaning Representation: The Case of Comparison Structures
Omid Bakhshandeh | James Allen
Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods

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Learning to Jointly Predict Ellipsis and Comparison Structures
Omid Bakhshandeh | Alexis Cornelia Wellwood | James Allen
Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning

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A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories
Nasrin Mostafazadeh | Nathanael Chambers | Xiaodong He | Devi Parikh | Dhruv Batra | Lucy Vanderwende | Pushmeet Kohli | James Allen
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2015

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Semantic Framework for Comparison Structures in Natural Language
Omid Bakhshandeh | James Allen
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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From Adjective Glosses to Attribute Concepts: Learning Different Aspects That an Adjective Can Describe
Omid Bakhshandeh | James Allen
Proceedings of the 11th International Conference on Computational Semantics

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Complex Event Extraction using DRUM
James Allen | Will de Beaumont | Lucian Galescu | Choh Man Teng
Proceedings of BioNLP 15

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SemEval-2015 Task 5: QA TempEval - Evaluating Temporal Information Understanding with Question Answering
Hector Llorens | Nathanael Chambers | Naushad UzZaman | Nasrin Mostafazadeh | James Allen | James Pustejovsky
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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Quantity, Contrast, and Convention in Cross-Situated Language Comprehension
Ian Perera | James Allen
Proceedings of the Nineteenth Conference on Computational Natural Language Learning

2014

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Learning a Lexicon for Broad-coverage Semantic Parsing
James Allen
Proceedings of the ACL 2014 Workshop on Semantic Parsing

2013

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Automatically Deriving Event Ontologies for a CommonSense Knowledge Base
James Allen | Will de Beaumont | Lucian Galescu | Jansen Orfan | Mary Swift | Choh Man Teng
Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Long Papers

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Automatic Metaphor Detection using Large-Scale Lexical Resources and Conventional Metaphor Extraction
Yorick Wilks | Adam Dalton | James Allen | Lucian Galescu
Proceedings of the First Workshop on Metaphor in NLP

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Plurality, Negation, and Quantification:Towards Comprehensive Quantifier Scope Disambiguation
Mehdi Manshadi | Daniel Gildea | James Allen
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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SemEval-2013 Task 1: TempEval-3: Evaluating Time Expressions, Events, and Temporal Relations
Naushad UzZaman | Hector Llorens | Leon Derczynski | James Allen | Marc Verhagen | James Pustejovsky
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

2012

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Expanding the Range of Tractable Scope-Underspecified Semantic Representations
Mehdi Manshadi | James Allen
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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An Annotation Scheme for Quantifier Scope Disambiguation
Mehdi Manshadi | James Allen | Mary Swift
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Annotating natural language sentences with quantifier scoping has proved to be very hard. In order to overcome the challenge, previous work on building scope-annotated corpora has focused on sentences with two explicitly quantified noun phrases (NPs). Furthermore, it does not address the annotation of scopal operators or complex NPs such as plurals and definites. We present the first annotation scheme for quantifier scope disambiguation where there is no restriction on the type or the number of scope-bearing elements in the sentence. We discuss some of the most prominent complex scope phenomena encountered in annotating the corpus, such as plurality and type-token distinction, and present mechanisms to handle those phenomena.

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Squibs: Fruit Carts: A Domain and Corpus for Research in Dialogue Systems and Psycholinguistics
Gregory Aist | Ellen Campana | James Allen | Mary Swift | Michael K. Tanenhaus
Computational Linguistics, Volume 38, Issue 3 - September 2012

2011

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Building Timelines from Narrative Clinical Records: Initial Results Based-on Deep Natural Language Understanding
Hyuckchul Jung | James Allen | Nate Blaylock | William de Beaumont | Lucian Galescu | Mary Swift
Proceedings of BioNLP 2011 Workshop

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Unrestricted Quantifier Scope Disambiguation
Mehdi Manshadi | James Allen
Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing

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A Corpus of Scope-disambiguated English Text
Mehdi Manshadi | James Allen | Mary Swift
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Temporal Evaluation
Naushad UzZaman | James Allen
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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TRIPS and TRIOS System for TempEval-2: Extracting Temporal Information from Text
Naushad UzZaman | James Allen
Proceedings of the 5th International Workshop on Semantic Evaluation

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Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading
Rutu Mulkar-Mehta | James Allen | Jerry Hobbs | Eduard Hovy | Bernardo Magnini | Chris Manning
Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading

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TRIOS-TimeBank Corpus: Extended TimeBank Corpus with Help of Deep Understanding of Text
Naushad UzZaman | James Allen
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

TimeBank (Pustejovsky et al, 2003a), a reference for TimeML (Pustejovsky et al, 2003b) compliant annotation, is widely used temporally annotated corpus in the community. It captures time expressions, events, and relations between events and event and temporal expression; but there is room for improvements in this hand-annotated widely used TimeBank corpus. This work is one such effort to extend the TimeBank corpus. Our first goal is to suggest missing TimeBank events and temporal expressions, i.e. events and temporal expressions that were missed by TimeBank annotators. Along with that this paper also suggests some additions to TimeML language by adding new event features (ontology type), some more SLINKs and also relations between events with their arguments, which we call RLINK (relation link). With our new suggestions we present the TRIOS-TimeBank corpus, an extended TimeBank corpus. We conclude by suggesting our future work to clean the TimeBank corpus even more and automatically generating larger temporally annotated corpus for the community.

2009

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TESLA: A Tool for Annotating Geospatial Language Corpora
Nate Blaylock | Bradley Swain | James Allen
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

2008

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Deep Semantic Analysis of Text
James F. Allen | Mary Swift | Will de Beaumont
Semantics in Text Processing. STEP 2008 Conference Proceedings

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Production in a Multimodal Corpus: how Speakers Communicate Complex Actions
Carlos Gómez Gallo | T. Florian Jaeger | James Allen | Mary Swift
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

We describe a new multimodal corpus currently under development. The corpus consists of videos of task-oriented dialogues that are annotated for speaker’s verbal requests and domain action executions. This resource provides data for new research on language production and comprehension. The corpus can be used to study speakers’ decisions as to how to structure their utterances given the complexity of the message they are trying to convey.

2007

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Demonstration of PLOW: A Dialogue System for One-Shot Task Learning
James Allen | Nathanael Chambers | George Ferguson | Lucian Galescu | Hyuckchul Jung | Mary Swift | William Taysom
Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT)

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Deep Linguistic Processing for Spoken Dialogue Systems
James Allen | Myroslava Dzikovska | Mehdi Manshadi | Mary Swift
ACL 2007 Workshop on Deep Linguistic Processing

2006

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Proceedings of the Third Workshop on Scalable Natural Language Understanding
James Allen | Jan Alexandersson | Jerome Feldman | Robert Porzel
Proceedings of the Third Workshop on Scalable Natural Language Understanding

2005

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A Collaborative Problem-Solving Model of Dialogue
Nate Blaylock | James Allen
Proceedings of the 6th SIGdial Workshop on Discourse and Dialogue

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Generic Parsing for Multi-Domain Semantic Interpretation
Myroslava Dzikovska | Mary Swift | James Allen | William de Beaumont
Proceedings of the Ninth International Workshop on Parsing Technology

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Online Statistics for a Unification-Based Dialogue Parser
Micha Elsner | Mary Swift | James Allen | Daniel Gildea
Proceedings of the Ninth International Workshop on Parsing Technology

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Two Diverse Systems Built using Generic Components for Spoken Dialogue (Recent Progress on TRIPS)
James Allen | George Ferguson | Amanda Stent | Scott Stoness | Mary Swift | Lucian Galescu | Nathan Chambers | Ellen Campana | Gregory Aist
Proceedings of the ACL Interactive Poster and Demonstration Sessions

2004

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Discourse Annotation in the Monroe Corpus
Joel Tetreault | Mary Swift | Preethum Prithviraj | Myroslava Dzikovska | James Allen
Proceedings of the Workshop on Discourse Annotation

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Incremental Parsing with Reference Interaction
Scott C. Stoness | Joel Tetreault | James Allen
Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together

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Stochastic Language Generation in a Dialogue System: Toward a Domain Independent Generator
Nathanael Chambers | James Allen
Proceedings of the 5th SIGdial Workshop on Discourse and Dialogue at HLT-NAACL 2004

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Semi-automatic Syntactic and Semantic Corpus Annotation with a Deep Parser
Mary D. Swift | Myroslava O. Dzikovska | Joel R. Tetreault | James F. Allen
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Skeletons in the parser: Using a shallow parser to improve deep parsing
Mary Swift | James Allen | Daniel Gildea
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2002

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Synchronization in an Asynchronous Agent-based architecture for Dialogue Systems
Nate Blaylock | James Allen | George Ferguson
Proceedings of the Third SIGdial Workshop on Discourse and Dialogue

2000

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TRIPS- 911 System Demonstration
James Allen | Donna Byron | Dave Costello | Myroslava Dzikovska | George Ferguson | Lucian Galescu | Amanda Stent
ANLP-NAACL 2000 Workshop: Conversational Systems

1999

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Speech repains, intonational phrases, and discourse markers: modeling speakers’ utterances in spoken dialogue
Peter A. Heeman | James F. Allen
Computational Linguistics, Volume 25, Number 4, December 1999

1997

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Intonational Boundaries, Speech Repairs, and Discourse Markers: Modeling Spoken Dialog
Peter A. Heeman | James F. Allen
35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics

1996

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A Robust System for Natural Spoken Dialogue
James F. Allen | Bradford W. Miller | Eric K. Ringger | Teresa Sikorski
34th Annual Meeting of the Association for Computational Linguistics

1994

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Discourse Obligations in Dialogue Processing
David R. Traum | James F. Allen
32nd Annual Meeting of the Association for Computational Linguistics

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Detecting and Correcting Speech Repairs
Peter Heeman | James Allen
32nd Annual Meeting of the Association for Computational Linguistics

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Tagging Speech Repairs
Peter A. Heeman | James Allen
Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994

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Natural Language Planning Dialogue for Interactive
James F. Allen | Len Schubert
Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994

1993

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Generic Plan Recognition for Dialogue Systems
George Ferguson | James F. Allen
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

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Natural Language Planning Dialogue for Intelligent Applications
James F. Allen | Len Schubert
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

1992

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Session I: Evaluating Spoken Language
James F. Allen
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

1991

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Session 5: Natural Language I
James F. Allen
Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991

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Discourse Structure in the TRAINS Project
James F. Allen
Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991

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Natural Language, Knowledge Representation and Discourse
James F. Allen | Lenhart K. Schubert
Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991

1990

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Natural Language, Knowledge Representation, and Discourse
James F. Allen | Lenhart K. Schubert
Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley, Pennsylvania, June 24-27,1990

1989

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Using Structural Constraints for Speech Act Interpretation
James F. Allen | Elizabeth Hinkelman
Speech and Natural Language: Proceedings of a Workshop Held at Cape Cod, Massachusetts, October 15-18, 1989

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Natural Language, Knowledge Representation and Discourse
James F. Allen | Lenhart K. Schubert
Speech and Natural Language: Proceedings of a Workshop Held at Cape Cod, Massachusetts, October 15-18, 1989

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Two Constraints on Speech Act Ambiguity
Elizabeth A. Hinkelman | James F. Allen
27th Annual Meeting of the Association for Computational Linguistics

1984

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A Plan Recognition Model for Clarification Subdialogues
Diane J. Litman | James F. Allen
10th International Conference on Computational Linguistics and 22nd Annual Meeting of the Association for Computational Linguistics

1982

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What’s in a Semantic Network?
James F. Allen | Alan M. Frisch
20th Annual Meeting of the Association for Computational Linguistics

1981

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What’s Necessary to Hide?: Modeling Action Verbs
James F. Allen
19th Annual Meeting of the Association for Computational Linguistics

1979

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Plans, Inference, and Indirect Speech Acts
James F. Allen
17th Annual Meeting of the Association for Computational Linguistics

1978

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Speech Acts as a Basis for Understanding Dialogue Coherence
C. Raymond Perrault | James F. Allen | Philip R. Cohen
American Journal of Computational Linguistics (December 1978)

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Speech Acts as a Basis for Understanding Dialogue Coherence
C. Raymond Perrault | James F. Allen
Theoretical Issues in Natural Language Processing-2