Bonnie Dorr

Also published as: Bonnie J. Dorr


2020

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Learning to Plan and Realize Separately for Open-Ended Dialogue Systems
Sashank Santhanam | Zhuo Cheng | Brodie Mather | Bonnie Dorr | Archna Bhatia | Bryanna Hebenstreit | Alan Zemel | Adam Dalton | Tomek Strzalkowski | Samira Shaikh
Findings of the Association for Computational Linguistics: EMNLP 2020

Achieving true human-like ability to conduct a conversation remains an elusive goal for open-ended dialogue systems. We posit this is because extant approaches towards natural language generation (NLG) are typically construed as end-to-end architectures that do not adequately model human generation processes. To investigate, we decouple generation into two separate phases: planning and realization. In the planning phase, we train two planners to generate plans for response utterances. The realization phase uses response plans to produce an appropriate response. Through rigorous evaluations, both automated and human, we demonstrate that decoupling the process into planning and realization performs better than an end-to-end approach.

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A New Approach to Parameter-Sharing in Multilingual Neural Machine Translation
Benyamin Ahmadnia | Bonnie Dorr
Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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Active Defense Against Social Engineering: The Case for Human Language Technology
Adam Dalton | Ehsan Aghaei | Ehab Al-Shaer | Archna Bhatia | Esteban Castillo | Zhuo Cheng | Sreekar Dhaduvai | Qi Duan | Bryanna Hebenstreit | Md Mazharul Islam | Younes Karimi | Amir Masoumzadeh | Brodie Mather | Sashank Santhanam | Samira Shaikh | Alan Zemel | Tomek Strzalkowski | Bonnie J. Dorr
Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management

We describe a system that supports natural language processing (NLP) components for active defenses against social engineering attacks. We deploy a pipeline of human language technology, including Ask and Framing Detection, Named Entity Recognition, Dialogue Engineering, and Stylometry. The system processes modern message formats through a plug-in architecture to accommodate innovative approaches for message analysis, knowledge representation and dialogue generation. The novelty of the system is that it uses NLP for cyber defense and engages the attacker using bots to elicit evidence to attribute to the attacker and to waste the attacker’s time and resources.

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Adaptation of a Lexical Organization for Social Engineering Detection and Response Generation
Archna Bhatia | Adam Dalton | Brodie Mather | Sashank Santhanam | Samira Shaikh | Alan Zemel | Tomek Strzalkowski | Bonnie J. Dorr
Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management

We present a paradigm for extensible lexicon development based on Lexical Conceptual Structure to support social engineering detection and response generation. We leverage the central notions of ask (elicitation of behaviors such as providing access to money) and framing (risk/reward implied by the ask). We demonstrate improvements in ask/framing detection through refinements to our lexical organization and show that response generation qualitatively improves as ask/framing detection performance improves. The paradigm presents a systematic and efficient approach to resource adaptation for improved task-specific performance.

2019

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Bilingual Low-Resource Neural Machine Translation with Round-Tripping: The Case of Persian-Spanish
Benyamin Ahmadnia | Bonnie Dorr
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

The quality of Neural Machine Translation (NMT), as a data-driven approach, massively depends on quantity, quality, and relevance of the training dataset. Such approaches have achieved promising results for bilingually high-resource scenarios but are inadequate for low-resource conditions. This paper describes a round-trip training approach to bilingual low-resource NMT that takes advantage of monolingual datasets to address training data scarcity, thus augmenting translation quality. We conduct detailed experiments on Persian-Spanish as a bilingually low-resource scenario. Experimental results demonstrate that this competitive approach outperforms the baselines.

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Enhancing Phrase-Based Statistical Machine Translation by Learning Phrase Representations Using Long Short-Term Memory Network
Benyamin Ahmadnia | Bonnie Dorr
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

Phrases play a key role in Machine Translation (MT). In this paper, we apply a Long Short-Term Memory (LSTM) model over conventional Phrase-Based Statistical MT (PBSMT). The core idea is to use an LSTM encoder-decoder to score the phrase table generated by the PBSMT decoder. Given a source sequence, the encoder and decoder are jointly trained in order to maximize the conditional probability of a target sequence. Analytically, the performance of a PBSMT system is enhanced by using the conditional probabilities of phrase pairs computed by an LSTM encoder-decoder as an additional feature in the existing log-linear model. We compare the performance of the phrase tables in the PBSMT to the performance of the proposed LSTM and observe its positive impact on translation quality. We construct a PBSMT model using the Moses decoder and enrich the Language Model (LM) utilizing an external dataset. We then rank the phrase tables using an LSTM-based encoder-decoder. This method produces a gain of up to 3.14 BLEU score on the test set.

2018

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Lexical Conceptual Structure of Literal and Metaphorical Spatial Language: A Case Study of “Push”
Bonnie Dorr | Mari Olsen
Proceedings of the First International Workshop on Spatial Language Understanding

Prior methodologies for understanding spatial language have treated literal expressions such as “Mary pushed the car over the edge” differently from metaphorical extensions such as “Mary’s job pushed her over the edge”. We demonstrate a methodology for standardizing literal and metaphorical meanings, by building on work in Lexical Conceptual Structure (LCS), a general-purpose representational component used in machine translation. We argue that spatial predicates naturally extend into other fields (e.g., circumstantial or temporal), and that LCS provides both a framework for distinguishing spatial from non-spatial, and a system for finding metaphorical meaning extensions. We start with MetaNet (MN), a large repository of conceptual metaphors, condensing 197 spatial entries into sixteen top-level categories of motion frames. Using naturally occurring instances of English push , and expansions of MN frames, we demonstrate that literal and metaphorical extensions exhibit patterns predicted and represented by the LCS model.

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The Case for Systematically Derived Spatial Language Usage
Bonnie Dorr | Clare Voss
Proceedings of the First International Workshop on Spatial Language Understanding

This position paper argues that, while prior work in spatial language understanding for tasks such as robot navigation focuses on mapping natural language into deep conceptual or non-linguistic representations, it is possible to systematically derive regular patterns of spatial language usage from existing lexical-semantic resources. Furthermore, even with access to such resources, effective solutions to many application areas such as robot navigation and narrative generation also require additional knowledge at the syntax-semantics interface to cover the wide range of spatial expressions observed and available to natural language speakers. We ground our insights in, and present our extensions to, an existing lexico-semantic resource, covering 500 semantic classes of verbs, of which 219 fall within a spatial subset. We demonstrate that these extensions enable systematic derivation of regular patterns of spatial language without requiring manual annotation.

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STYLUS: A Resource for Systematically Derived Language Usage
Bonnie Dorr | Clare Voss
Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing

We describe a resource derived through extraction of a set of argument realizations from an existing lexical-conceptual structure (LCS) Verb Database of 500 verb classes (containing a total of 9525 verb entries) to include information about realization of arguments for a range of different verb classes. We demonstrate that our extended resource, called STYLUS (SysTematicallY Derived Language USe), enables systematic derivation of regular patterns of language usage without requiring manual annotation. We posit that both spatially oriented applications such as robot navigation and more general applications such as narrative generation require a layered representation scheme where a set of primitives (often grounded in space/motion such as GO) is coupled with a representation of constraints at the syntax-semantics interface. We demonstrate that the resulting resource covers three cases of lexico-semantic operations applicable to both language understanding and language generation.

2017

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Characterization of Divergence in Impaired Speech of ALS Patients
Archna Bhatia | Bonnie Dorr | Kristy Hollingshead | Samuel L. Phillips | Barbara McKenzie
BioNLP 2017

Approximately 80% to 95% of patients with Amyotrophic Lateral Sclerosis (ALS) eventually develop speech impairments, such as defective articulation, slow laborious speech and hypernasality. The relationship between impaired speech and asymptomatic speech may be seen as a divergence from a baseline. This relationship can be characterized in terms of measurable combinations of phonological characteristics that are indicative of the degree to which the two diverge. We demonstrate that divergence measurements based on phonological characteristics of speech correlate with physiological assessments of ALS. Speech-based assessments offer benefits over commonly-used physiological assessments in that they are inexpensive, non-intrusive, and do not require trained clinical personnel for administering and interpreting the results.

2013

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Computing Lexical Contrast
Saif M. Mohammad | Bonnie J. Dorr | Graeme Hirst | Peter D. Turney
Computational Linguistics, Volume 39, Issue 3 - September 2013

2012

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Modality and Negation in SIMT Use of Modality and Negation in Semantically-Informed Syntactic MT
Kathryn Baker | Michael Bloodgood | Bonnie J. Dorr | Chris Callison-Burch | Nathaniel W. Filardo | Christine Piatko | Lori Levin | Scott Miller
Computational Linguistics, Volume 38, Issue 2 - June 2012

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Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing
Vinodkumar Prabhakaran | Michael Bloodgood | Mona Diab | Bonnie Dorr | Lori Levin | Christine D. Piatko | Owen Rambow | Benjamin Van Durme
Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics

2010

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Putting the User in the Loop: Interactive Maximal Marginal Relevance for Query-Focused Summarization
Jimmy Lin | Nitin Madnani | Bonnie Dorr
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Generating Phrasal and Sentential Paraphrases: A Survey of Data-Driven Methods
Nitin Madnani | Bonnie J. Dorr
Computational Linguistics, Volume 36, Issue 3 - September 2010

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A Modality Lexicon and its use in Automatic Tagging
Kathryn Baker | Michael Bloodgood | Bonnie Dorr | Nathaniel W. Filardo | Lori Levin | Christine Piatko
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper describes our resource-building results for an eight-week JHU Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically-Informed Machine Translation. Specifically, we describe the construction of a modality annotation scheme, a modality lexicon, and two automated modality taggers that were built using the lexicon and annotation scheme. Our annotation scheme is based on identifying three components of modality: a trigger, a target and a holder. We describe how our modality lexicon was produced semi-automatically, expanding from an initial hand-selected list of modality trigger words and phrases. The resulting expanded modality lexicon is being made publicly available. We demonstrate that one tagger―a structure-based tagger―results in precision around 86% (depending on genre) for tagging of a standard LDC data set. In a machine translation application, using the structure-based tagger to annotate English modalities on an English-Urdu training corpus improved the translation quality score for Urdu by 0.3 Bleu points in the face of sparse training data.

2009

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Generating High-Coverage Semantic Orientation Lexicons From Overtly Marked Words and a Thesaurus
Saif Mohammad | Cody Dunne | Bonnie Dorr
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Fluency, Adequacy, or HTER? Exploring Different Human Judgments with a Tunable MT Metric
Matthew Snover | Nitin Madnani | Bonnie Dorr | Richard Schwartz
Proceedings of the Fourth Workshop on Statistical Machine Translation

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Using Citations to Generate surveys of Scientific Paradigms
Saif Mohammad | Bonnie Dorr | Melissa Egan | Ahmed Hassan | Pradeep Muthukrishan | Vahed Qazvinian | Dragomir Radev | David Zajic
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

2008

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The ACL Anthology Reference Corpus: A Reference Dataset for Bibliographic Research in Computational Linguistics
Steven Bird | Robert Dale | Bonnie Dorr | Bryan Gibson | Mark Joseph | Min-Yen Kan | Dongwon Lee | Brett Powley | Dragomir Radev | Yee Fan Tan
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

The ACL Anthology is a digital archive of conference and journal papers in natural language processing and computational linguistics. Its primary purpose is to serve as a reference repository of research results, but we believe that it can also be an object of study and a platform for research in its own right. We describe an enriched and standardized reference corpus derived from the ACL Anthology that can be used for research in scholarly document processing. This corpus, which we call the ACL Anthology Reference Corpus (ACL ARC), brings together the recent activities of a number of research groups around the world. Our goal is to make the corpus widely available, and to encourage other researchers to use it as a standard testbed for experiments in both bibliographic and bibliometric research.

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Combining Open-Source with Research to Re-engineer a Hands-on Introductory NLP Course
Nitin Madnani | Bonnie J. Dorr
Proceedings of the Third Workshop on Issues in Teaching Computational Linguistics

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Language and Translation Model Adaptation using Comparable Corpora
Matthew Snover | Bonnie Dorr | Richard Schwartz
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

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Computing Word-Pair Antonymy
Saif Mohammad | Bonnie Dorr | Graeme Hirst
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2007

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Combining Outputs from Multiple Machine Translation Systems
Antti-Veikko Rosti | Necip Fazil Ayan | Bing Xiang | Spyros Matsoukas | Richard Schwartz | Bonnie Dorr
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

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Using Paraphrases for Parameter Tuning in Statistical Machine Translation
Nitin Madnani | Necip Fazil Ayan | Philip Resnik | Bonnie Dorr
Proceedings of the Second Workshop on Statistical Machine Translation

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Measuring Variability in Sentence Ordering for News Summarization
Nitin Madnani | Rebecca Passonneau | Necip Fazil Ayan | John Conroy | Bonnie Dorr | Judith Klavans | Dianne O’Leary | Judith Schlesinger
Proceedings of the Eleventh European Workshop on Natural Language Generation (ENLG 07)

2006

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Going Beyond AER: An Extensive Analysis of Word Alignments and Their Impact on MT
Necip Fazil Ayan | Bonnie J. Dorr
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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PCFGs with Syntactic and Prosodic Indicators of Speech Repairs
John Hale | Izhak Shafran | Lisa Yung | Bonnie J. Dorr | Mary Harper | Anna Krasnyanskaya | Matthew Lease | Yang Liu | Brian Roark | Matthew Snover | Robin Stewart
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Leveraging Reusability: Cost-Effective Lexical Acquisition for Large-Scale Ontology Translation
G. Craig Murray | Bonnie J. Dorr | Jimmy Lin | Jan Hajič | Pavel Pecina
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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SParseval: Evaluation Metrics for Parsing Speech
Brian Roark | Mary Harper | Eugene Charniak | Bonnie Dorr | Mark Johnson | Jeremy Kahn | Yang Liu | Mari Ostendorf | John Hale | Anna Krasnyanskaya | Matthew Lease | Izhak Shafran | Matthew Snover | Robin Stewart | Lisa Yung
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

While both spoken and written language processing stand to benefit from parsing, the standard Parseval metrics (Black et al., 1991) and their canonical implementation (Sekine and Collins, 1997) are only useful for text. The Parseval metrics are undefined when the words input to the parser do not match the words in the gold standard parse tree exactly, and word errors are unavoidable with automatic speech recognition (ASR) systems. To fill this gap, we have developed a publicly available tool for scoring parses that implements a variety of metrics which can handle mismatches in words and segmentations, including: alignment-based bracket evaluation, alignment-based dependency evaluation, and a dependency evaluation that does not require alignment. We describe the different metrics, how to use the tool, and the outcome of an extensive set of experiments on the sensitivity.

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Parallel Syntactic Annotation of Multiple Languages
Owen Rambow | Bonnie Dorr | David Farwell | Rebecca Green | Nizar Habash | Stephen Helmreich | Eduard Hovy | Lori Levin | Keith J. Miller | Teruko Mitamura | Florence Reeder | Advaith Siddharthan
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

This paper describes an effort to investigate the incrementally deepening development of an interlingua notation, validated by human annotation of texts in English plus six languages. We begin with deep syntactic annotation, and in this paper present a series of annotation manuals for six different languages at the deep-syntactic level of representation. Many syntactic differences between languages are removed in the proposed syntactic annotation, making them useful resources for multilingual NLP projects with semantic components.

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Leveraging Recurrent Phrase Structure in Large-scale Ontology Translation
G. Craig Murray | Bonnie J. Dorr | Jimmy Lin | Jan Hajič | Pavel Pecina
Proceedings of the 11th Annual conference of the European Association for Machine Translation

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A Maximum Entropy Approach to Combining Word Alignments
Necip Fazil Ayan | Bonnie J. Dorr
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference

2005

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NeurAlign: Combining Word Alignments Using Neural Networks
Necip Fazil Ayan | Bonnie J. Dorr | Christof Monz
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

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Alignment Link Projection Using Transformation-Based Learning
Necip Fazil Ayan | Bonnie J. Dorr | Christof Monz
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

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A Methodology for Extrinsic Evaluation of Text Summarization: Does ROUGE Correlate?
Bonnie Dorr | Christof Monz | Stacy President | Richard Schwartz | David Zajic
Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization

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Frame Semantic Enhancement of Lexical-Semantic Resources
Rebecca Green | Bonnie Dorr
Proceedings of the ACL-SIGLEX Workshop on Deep Lexical Acquisition

2004

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Inducing Frame Semantic Verb Classes from WordNet and LDOCE
Rebecca Green | Bonnie J. Dorr | Philip Resnik
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

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Identification of Confusable Drug Names: A New Approach and Evaluation Methodology
Grzegorz Kondrak | Bonnie Dorr
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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A Lexically-Driven Algorithm for Disfluency Detection
Matthew Snover | Bonnie Dorr | Richard Schwartz
Proceedings of HLT-NAACL 2004: Short Papers

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Inducing a semantic frame lexicon from WordNet data
Rebecca Green | Bonnie Dorr
Proceedings of the 2nd Workshop on Text Meaning and Interpretation

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Interlingual Annotation of Multilingual Text Corpora
Stephen Helmreich | David Farwell | Bonnie Dorr | Nizar Habash | Lori Levin | Teruko Mitamura | Florence Reeder | Keith Miller | Eduard Hovy | Owen Rambow | Advaith Siddharthan
Proceedings of the Workshop Frontiers in Corpus Annotation at HLT-NAACL 2004

2003

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Hedge Trimmer: A Parse-and-Trim Approach to Headline Generation
Bonnie Dorr | David Zajic | Richard Schwartz
Proceedings of the HLT-NAACL 03 Text Summarization Workshop

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A Categorial Variation Database for English
Nizar Habash | Bonnie Dorr
Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics

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Desparately Seeking Cebuano
Douglas W. Oard | David Doermann | Bonnie Dorr | Daqing He | Philip Resnik | Amy Weinberg | William Byrne | Sanjeev Khudanpur | David Yarowsky | Anton Leuski | Philipp Koehn | Kevin Knight
Companion Volume of the Proceedings of HLT-NAACL 2003 - Short Papers

2001

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Mapping Lexical Entries in a Verbs Database to WordNet Senses
Rebecca Green | Lisa Pearl | Bonnie J. Dorr | Philip Resnik
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics

2000

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Chinese-English Semantic Resource Construction
Bonnie J. Dorr | Gina-Anne Levow | Dekang Lin | Scott Thomas
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

1997

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Structured Lexicons and Semantic Tagging
Bonnie J. Dorr | Mari Broman Olsen
Tagging Text with Lexical Semantics: Why, What, and How?

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Deriving Verbal and Compositonal Lexical Aspect for NLP Applications
Bonnie J. Dorr | Mari Broman Olsen
35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics

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Large-Scale Acquisition of LCS-Based Lexicons for Foreign Language Tutoring
Bonnie J. Dorr
Fifth Conference on Applied Natural Language Processing

1996

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Role of Word Sense Disalnbiguation in Lexical Acquisition: Predicting Semantics from Syntactic Cues
Bonnie J. Dorr | Doug Jones
COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics

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Acquisition of Semantic Lexicons: Using Word Sense Disambiguation to Improve Precision
Bonnie J. Dorr | Doug Jones
Breadth and Depth of Semantic Lexicons

1995

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Squibs and Discussions: Efficient Parsing for Korean and English: A Parameterized Message-Passing Approach
Bonnie J. Dorr | Jye-hoon Lee | Dekang Lin | Sungki Suh
Computational Linguistics, Volume 21, Number 2, June 1995

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Book Reviews: Compositional translation
Bonnie J. Dorr
Computational Linguistics, Volume 21, Number 4, December 1995

1994

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Machine Translation Divergences: A Formal Description and Proposed Solution
Bonnie J. Dorr
Computational Linguistics, Volume 20, Number 4, December 1994

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The Case for a MT Developers’ Tool with a Two-Component View of the Interlingua
Bonnie Dorr | Clare Voss
Proceedings of the First Conference of the Association for Machine Translation in the Americas

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A Parameter-Based Message-Passing Parser for MT of Korean and English
Dekang Lin | Bonnie Dorr | Jye-hoon Lee | Sungki Suh
Proceedings of the First Conference of the Association for Machine Translation in the Americas

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Is MT Research Doing Any Good?
Kenneth Church | Bonnie Dorr | Eduard Hovy | Sergei Nirenburg | Bernard Scott | Virginia Teller
Proceedings of the First Conference of the Association for Machine Translation in the Americas

1992

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A Parameterized Approach to Integrating Aspect With Lexical-Semantics for Machine Translation
Bonnie J. Dorr
30th Annual Meeting of the Association for Computational Linguistics

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Parameterization of the Interlingua in Machine Translation
Bonnie Dorr
COLING 1992 Volume 2: The 15th International Conference on Computational Linguistics

1991

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A Two-Level Knowledge Representation for Machine Translation: Lexical Semantics and Tense/Aspect
Bonnie J. Dorr
Lexical Semantics and Knowledge Representation

1990

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Solving Thematic Divergences in Machine Translation
Bonnie Dorr
28th Annual Meeting of the Association for Computational Linguistics

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