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Hits 1 – 20 of 76
1
From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and Domains ...
Mather, Brodie
;
Dorr, Bonnie J
;
Dalton, Adam
. - : arXiv, 2022
BASE
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2
Detecting Asks in SE attacks: Impact of Linguistic and Structural Knowledge ...
Dorr, Bonnie J.
;
Bhatia, Archna
;
Dalton, Adam
. - : arXiv, 2020
BASE
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3
Adaptation of a Lexical Organization for Social Engineering Detection and Response Generation ...
Bhatia, Archna
;
Dalton, Adam
;
Mather, Brodie
. - : arXiv, 2020
BASE
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4
Use of Modality and Negation in Semantically-Informed Syntactic MT ...
Baker, Kathryn
;
Bloodgood, Michael
;
Dorr, Bonnie J.
. - : arXiv, 2015
BASE
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5
A Modality Lexicon and its use in Automatic Tagging ...
Baker, Kathryn
;
Bloodgood, Michael
;
Dorr, Bonnie J.
. - : arXiv, 2014
BASE
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6
Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach ...
Baker, Kathryn
;
Bloodgood, Michael
;
Callison-Burch, Chris
. - : arXiv, 2014
BASE
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7
Computing Lexical Contrast ...
Mohammad, Saif M.
;
Dorr, Bonnie J.
;
Hirst, Graeme
. - : arXiv, 2013
BASE
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8
Use of Modality and Negation in Semantically-Informed Syntactic MT
Baker, Kathryn
;
Bloodgood, Michael
;
Dorr, Bonnie J
...
In: DTIC (2012)
BASE
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9
Measuring Degrees of Semantic Opposition ...
Mohammad, Saif M.
;
Dorr, Bonnie J.
;
Hirst, Graeme
. - : National Research Council Canada, 2011
BASE
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10
The ACL Anthology Reference Corpus: A Reference Dataset for Bibliographic Research in Computational Linguistics
Bird, Steven
;
Dale, Robert
;
Dorr, Bonnie J
. - : Paris : European Language Resources Association (ELRA), 2008
BASE
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11
Measuring Variability in Sentence Ordering for News Summarization
Klavans, Judith L.
;
Madnani, Nitin
;
Passonneau, Rebecca
. - : Proceeding ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation, 2007
BASE
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12
Multiple Alternative Sentene Compressions as a Tool for Automatic Summarization Tasks
Zajic, David Michael
. - 2007
BASE
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13
Text Summarization Evaluation: Correlating Human Performance on an Extrinsic Task with Automatic Intrinsic Metrics
Hobson, Stacy
. - 2007
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14
Text Summarization Evaluation: Correlating Human Performance on an Extrinsic Task with Automatic Intrinsic Metrics
President, Stacy F.
;
Dorr, Bonnie J.
In: DTIC (2006)
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15
Deriving verbal and compositional lexical aspect for NLP applications
Dorr, Bonnie J.
;
Olsen, Mari B.
In:
The language of time
(Oxford, 2005), p. 115-128
MPI für Psycholinguistik
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16
Combining Linguistic and Machine Learning Techniques for Word Alignment Improvement
Ayan, Necip Fazil
. - 2005
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17
Use of Minimal Lexical Conceptual Structures for Single-Document Summarization
Dorr, Bonnie J.
;
Habash, Nizar Y.
;
Monz, Christof
...
In: DTIC (2004)
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18
Symbolic MT With Statistical NLP Components
Dorr, Bonnie J.
;
Habash, Nizar Y.
;
Monz, Christof
In: DTIC (2004)
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19
Inducing Semantic Frames from Lexical Resources
Green, Rebecca Joyce
. - 2004
Abstract:
The multiple ways in which propositional content can be expressed is often referred to as the paraphrase problem. This phenomenon creates challenges for such applications as information retrieval, information extraction, text summarization, and machine translation: Natural language understanding needs to recognize what remains constant across paraphrases, while natural language generation needs the ability to express content in various ways. Frame semantics is a theory of language understanding that addresses the paraphrase problem by providing slot-and-filler templates to represent frequently occurring, structured experiences. This dissertation introduces SemFrame, a system that induces semantic frames automatically from lexical resources (WordNet and the Longman Dictionary of Contemporary English [LDOCE]). Prior to SemFrame, semantic frames had been developed only by hand. In SemFrame, frames are first identified by enumerating groups of verb senses that evoke a common frame. This is done by combining evidence about pairs of semantically related verbs, based on LDOCE's subject field codes, words used in LDOCE definitions and WordNet glosses, WordNet's array of semantic relationships, etc. Pairs are gathered into larger groupings, deemed to correspond to semantic frames. Nouns associated with the verbs evoking a frame are then analyzed against WordNet's semantic network to identify nodes corresponding to frame slots. SemFrame is evaluated in two ways: (1) Compared against the handcrafted FrameNet, SemFrame achieves its best recall-precision balance with 83.2% recall (based on SemFrame's coverage of FrameNet frames) and 73.8% precision (based on SemFrame verbs' semantic relatedness to other frame-evoking verbs). A WordNet-hierarchy-based lower bound achieves 52.8% recall and 46.6% precision. (2) A frame-semantic-enhanced version of Hearst's TextTiling algorithm, applied to detecting boundaries between consecutive documents, improves upon the non-enhanced TextTiling algorithm at statistically significant levels. (Previous enhancement of the text segmentation algorithm with thesaural relationships had degraded performance.)
Keyword:
Artificial Intelligence
;
Computer Science
;
Language
;
Linguistics
URL:
http://hdl.handle.net/1903/193
BASE
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20
Use of OCR for Rapid Construction of Bilingual Lexicons
Karagol-Ayan, Burcu
;
Doermann, David
;
Dorr, Bonnie J.
In: DTIC (2003)
BASE
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