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Hits 1 – 20 of 70
1
Automatic Dialect Density Estimation for African American English ...
Johnson, Alexander
;
Everson, Kevin
;
Ravi, Vijay
. - : arXiv, 2022
BASE
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2
DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Hajishirzi, Hannaneh
;
Lu, Bo-Ru
. - : Underline Science Inc., 2021
BASE
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3
Dialogue State Tracking with a Language Model using Schema-Driven Prompting ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Cheng, Hao
;
Lee, Chia-Hsuan
. - : Underline Science Inc., 2021
BASE
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4
A Controllable Model of Grounded Response Generation ...
Wu, Zeqiu
;
Galley, Michel
;
Brockett, Chris
. - : arXiv, 2020
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5
Neural Models for Integrating Prosody in Spoken Language Understanding
Tran, Trang
. - 2020
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6
Automatic Analysis of Language Use in K-16 STEM Education and Impact on Student Performance
Nadeem, Farah
. - 2020
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7
Asynchronous Speech Recognition Affects Physician Editing of Notes
Lybarger, Kevin J.
;
Ostendorf, Mari
;
Riskin, Eve
. - : Georg Thieme Verlag KG, 2018
BASE
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8
Low-Rank RNN Adaptation for Context-Aware Language Modeling
Jaech, Aaron
. - 2018
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9
Parsing Speech: A Neural Approach to Integrating Lexical and Acoustic-Prosodic Information ...
Tran, Trang
;
Toshniwal, Shubham
;
Bansal, Mohit
. - : arXiv, 2017
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10
Effective Use of Cross-Domain Parsing in Automatic Speech Recognition and Error Detection
Marin, Marius
. - 2015
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11
Automatic Characterization of Text Difficulty
Medero, Julie
. - 2014
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12
Data Selection for Statistical Machine Translation
Axelrod, Amittai
. - 2014
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13
Graph-based query strategies for active learning
Wu, Wei
;
Ostendorf, Mari
In:
Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst.
21 (2013) 2, 260-269
OLC Linguistik
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14
Rank and Sparsity in Language Processing
Hutchinson, Brian
. - 2013
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15
Joint reranking of parsing and word recognition with automatic segmentation
Ostendorf, Mari
;
Kahn, Jeremy G.
In:
Computer speech and language. - Amsterdam [u.a.] : Elsevier
26 (2012) 1, 1-19
BLLDB
OLC Linguistik
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16
Graph-based Algorithms for Lexical Semantics and its Applications
Wu, Wei
. - 2012
Abstract:
Thesis (Ph.D.)--University of Washington, 2012 ; Lexical semantics studies the meaning of words, which is a useful tool for computer-based automatic natural language processing (NLP). This thesis explores graph-based algorithms to learn and apply distributional lexical semantics in NLP applications. One theory of lexical semanticists holds that semantic relations among words can be extracted from their textual context in natural languages. Based on this theory, we propose using graphs to represent natural language text according to the contextual relations of words in higher-level language units (e.g. sentences, definitions or documents). In these graphs, words and/or higher-level language units are represented with nodes, and edges are added between them according to their textual context to indicate their observed relatedness in a dictionary or a collection of documents. We explore two types of graph representations: the word-word graph which is used for modeling the semantic relations among words, and the instance-word bipartite graph which uses words as a medium to study the relatedness among higher-level language units. In this way, we can embed the semantic relations among words and optionally higher-level language units into the graph structure. We design algorithms to propagate semantic information through the graphs in order to recover the unobserved relatedness among words or higher-level language units, which is used in designing unsupervised, semi-supervised or active learning algorithms to reduce human supervision in NLP applications for harvesting or analyzing text data resources. Specifically, we design graph-based algorithms either for quantitatively assessing lexical semantic similarity, or for developing representativeness and diversity criteria for selecting a characteristic subset of terms, which is useful for problems such as keyword summarization and query design for active learning. In particular, we focus on designing graph-based algorithms to apply lexical semantics in three NLP applications, including Wiktionary lexical semantic similarity extraction, Twitter user interest extraction, and active learning for semantic orientation classification.
Keyword:
Computer science
;
Electrical engineering
;
Graph-based Algorithms
;
Lexical Semantics
;
NLP
;
Semantic Similarity
;
Sentiment Classification
;
Twitter
URL:
http://hdl.handle.net/1773/20590
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17
Expected dependency pair match: predicting translation quality with expected syntactic structure
Ostendorf, Mari
;
Kahn, Jeremy G.
;
Snover, Matthew G.
In:
Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media
23 (2010) 2-3, 169-179
BLLDB
OLC Linguistik
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18
A machine learning approach to reading level assessment
Petersen, Sarah E.
;
Ostendorf, Mari
In:
Computer speech and language. - Amsterdam [u.a.] : Elsevier
23 (2009) 1, 89-106
OLC Linguistik
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19
A machine learning approach to reading level assessment
Petersen, Sarah E.
;
Ostendorf, Mari
In:
Computer speech and language. - Amsterdam [u.a.] : Elsevier
23 (2009) 1, 89-106
BLLDB
OLC Linguistik
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20
Improving robustness of MLLR adaptation with speaker-clustered regression class trees
Mandal, Arindam
;
Ostendorf, Mari
;
Stolcke, Andreas
In:
Computer speech and language. - Amsterdam [u.a.] : Elsevier
23 (2009) 2, 176-199
BLLDB
OLC Linguistik
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