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Hits 1 – 20 of 79
1
Parallel processing in speech perception with local and global representations of linguistic context
Brodbeck, Christian
;
Bhattasali, Shohini
;
Cruz Heredia, Aura AL
...
In: eLife (2022)
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2
Using surprisal and fMRI to map the neural bases of broad and local contextual prediction during natural language comprehension ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Bhattasali, Shohini
;
Resnik, Philip
. - : Underline Science Inc., 2021
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3
Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task
MacAvaney, Sean
;
Mittu, Anjali
;
Coppersmith, Glen
. - 2021
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4
Connecting Documents, Words, and Languages Using Topic Models
Yang, Weiwei
. - 2019
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5
Assessing Composition in Sentence Vector Representations ...
Ettinger, Allyson
;
Elgohary, Ahmed
;
Phillips, Colin
. - : arXiv, 2018
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6
Relating lexical and syntactic processes in language: Bridging research in humans and machines
Ettinger, Allyson
. - 2018
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7
Guided Probabilistic Topic Models for Agenda-setting and Framing
Nguyen, Viet An
. - 2015
Abstract:
Probabilistic topic models are powerful methods to uncover hidden thematic structures in text by projecting each document into a low dimensional space spanned by a set of topics. Given observed text data, topic models infer these hidden structures and use them for data summarization, exploratory analysis, and predictions, which have been applied to a broad range of disciplines. Politics and political conflicts are often captured in text. Traditional approaches to analyze text in political science and other related fields often require close reading and manual labeling, which is labor-intensive and hinders the use of large-scale collections of text. Recent work, both in computer science and political science, has used automated content analysis methods, especially topic models to substantially reduce the cost of analyzing text at large scale. In this thesis, we follow this approach and develop a series of new probabilistic topic models, guided by additional information associated with the text, to discover and analyze agenda-setting (i.e., what topics people talk about) and framing (i.e., how people talk about those topics), a central research problem in political science, communication, public policy and other related fields. We first focus on study agendas and agenda control behavior in political debates and other conversations. The model we introduce, Speaker Identity for Topic Segmentation (SITS), is able to discover what topics that are talked about during the debates, when these topics change, and a speaker-specific measure of agenda control. To make the analysis process more effective, we build Argviz, an interactive visualization which leverages SITS's outputs to allow users to quickly grasp the conversational topic dynamics, discover when the topic changes and by whom, and interactively visualize the conversation's details on demand. We then analyze policy agendas in a more general setting of political text. We present the Label to Hierarchy (L2H) model to learn a hierarchy of topics from multi-labeled data, in which each document is tagged with multiple labels. The model captures the dependencies among labels using an interpretable tree-structured hierarchy, which helps provide insights about the political attentions that policymakers focus on, and how these policy issues relate to each other. We then go beyond just agenda-setting and expand our focus to framing--the study of how agenda issues are talked about, which can be viewed as second-level agenda-setting. To capture this hierarchical views of agendas and frames, we introduce the Supervised Hierarchical Latent Dirichlet Allocation (SHLDA) model, which jointly captures a collection of documents, each is associated with a continuous response variable such as the ideological position of the document's author on a liberal-conservative spectrum. In the topic hierarchy discovered by SHLDA, higher-level nodes map to more general agenda issues while lower-level nodes map to issue-specific frames. Although qualitative analysis shows that the topic hierarchies learned by SHLDA indeed capture the hierarchical view of agenda-setting and framing motivating the work, interpreting the discovered hierarchy still incurs moderately high cost due to the complex and abstract nature of framing. Motivated by improving the hierarchy, we introduce Hierarchical Ideal Point Topic Model (HIPTM) which jointly models a collection of votes (e.g., congressional roll call votes) and both the text associated with the voters (e.g., members of Congress) and the items (e.g., congressional bills). Customized specifically for capturing the two-level view of agendas and frames, HIPTM learns a two-level hierarchy of topics, in which first-level nodes map to an interpretable policy issue and second-level nodes map to issue-specific frames. In addition, instead of using pre-computed response variable, HIPTM also jointly estimates the ideological positions of voters on multiple interpretable dimensions.
Keyword:
Agenda setting
;
Computational Social Science
;
Computer science
;
Framing
;
Machine Learning
;
Natural Language Processing
URL:
http://hdl.handle.net/1903/16600
https://doi.org/10.13016/M2H056
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8
Soft syntactic constraints for Arabic-English hierarchical phrase-based translation
Resnik, Philip
;
Chiang, David
;
Marton, Yuval
In:
Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media
26 (2012) 1-2, 137-157
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9
Crowdsourced Monolingual Translation
Hu, Chang
. - 2012
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10
Decision Tree-based Syntactic Language Modeling
Filimonov, Denis
. - 2011
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11
Modeling Dependencies in Natural Languages with Latent Variables
Huang, Zhongqiang
. - 2011
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12
Exploiting syntactic relationships in a phrase-based decoder: an exploration
Hunter, Tim
;
Resnik, Philip
In:
Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media
24 (2010) 2, 123-140
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OLC Linguistik
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13
Gibbs Sampling for the Uninitiated
Resnik, Philip
;
Hardisty, Eric
In: DTIC (2010)
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14
Structured local exponential models for machine translation
Subotin, Michael
. - 2010
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15
A Formal Model of Ambiguity and its Applications in Machine Translation
Dyer, Christopher
. - 2010
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16
Extending Phrase-Based Decoding with a Dependency-Based Reordering Model
Hunter, Tim
;
Resnik, Philip
In: DTIC (2009)
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17
Extending Phrase-Based Decoding with a Dependency-Based Reordering Model
Resnik, Philip
;
Hunter, Tim
. - 2009
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18
COMPUTATIONAL ANALYSIS OF THE CONVERSATIONAL DYNAMICS OF THE UNITED STATES SUPREME COURT
Hawes, Timothy
. - 2009
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19
Fine-Grained Linguistic Soft Constraints on Statistical Natural Language Processing Models
Marton, Yuval Yehezkel
. - 2009
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20
Generalizing Word Lattice Translation
Dyer, Christopher
;
Muresan, Smaranda
;
Resnik, Philip
In: DTIC (2008)
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