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1
QUT Para at TREC 2012 Web Track: Word Associations for Retrieving Web Documents
In: DTIC (2012)
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2
Linguistic Model for Axle Fatigue
In: DTIC (2012)
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3
Phoneme Class Based Adaptation for Mismatch Acoustic Modeling of Distant Noisy Speech (Preprint)
In: DTIC (2012)
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4
Conference Report: Cultural and Linguistic Advancement for Mission Success: Enhancing Language, Regional and Cultural Capabilities Across Whole of Government for an Effective COIN Strategy
In: DTIC (2012)
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5
Familiar Speaker Recognition
In: DTIC (2012)
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6
Machine Recognition vs Human Recognition of Voices
In: DTIC (2012)
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7
Speaker Clustering for a Mixture of Singing and Reading (Preprint)
In: DTIC (2012)
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8
Applications of Lexical Link Analysis Web Service for Large-Scale Automation, Validation, Discovery, Visualization, and Real-Time Program-Awareness
In: DTIC (2012)
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9
Compressed Domain Automatic Level Control Based on ITU-T G.722.2
In: DTIC (2012)
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10
Integrating Hard and Soft Information Sources for D2D Using Controlled Natural Language
In: DTIC (2012)
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11
SAWUS: Siena's Automatic Wikipedia Update System
In: DTIC (2012)
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12
Aligning Learning Capability with Strategy: A Training Needs Assessment (TNA) Case Study
In: DTIC (2012)
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13
Learning for Microblogs with Distant Supervision: Political Forecasting with Twitter
In: DTIC (2012)
Abstract: Microblogging websites such as Twitter offer a wealth of insight into a population's current mood. Automated approaches to identifying general sentiment toward a particular topic often perform two steps: Topic Identification and Sentiment Analysis. Topic Identification identifies tweets that are relevant to a desired topic (e.g., a politician or event), and Sentiment Analysis extracts each tweet's attitude toward the topic. Many techniques for Topic Identification simply involve selecting tweets using a keyword search. Here we present an approach that uses distant supervision to train a classifier on the tweets returned by the search. We show that distant supervision leads to improved performance in the Topic Identification task as well as in the downstream Sentiment Analysis task. We then use a system that incorporates distant supervision into both stages to analyze sentiments toward President Obama as expressed in a dataset of tweets. That is, we apply our approach to the problem of predicting Presidential Job Approval polls from Twitter data. Our results show better correlation with Gallup's Presidential Job Approval polls than previous work. We also present a novel baseline that performs remarkably well without using Topic Identification. ; Presented at the 13th Conference of the European Chapter of the Association for Computational Linguistics held in Avignon, France, on April 23-27, 2012 (p603-612).
Keyword: *ATTITUDES(PSYCHOLOGY); *CLASSIFICATION; *DISTANT SUPERVISION; *FORECASTING; *IDENTIFICATION; *LEARNING MACHINES; *MICROBLOGS; *ONLINE COMMUNITIES; *POLITICAL FORECASTING; *SENTIMENT ANALYSIS; *TOPIC IDENTIFICATION; *TRAINING; *TWITTER; COMPUTATIONAL LINGUISTICS; Computer Systems; Cybernetics; EMOTICONS; EMOTIONS; INTERNET; KEYWORD SELECTION; Linguistics; MACHINE LEARNING; OPINION ANALYSIS; POLARITY; POLITICAL MOOD; POLITICAL SENTIMENT; POLITICAL TWEETS; PREDICTIONS; Psychology; PUBLIC OPINION; SENTIMENT CLASSIFICATION; SOCIAL MEDIA; SOCIAL NETWORKS; SYMPOSIA; TEXT PROCESSING; WORDS(LANGUAGE)
URL: http://www.dtic.mil/docs/citations/ADA589957
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA589957
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14
Kommunikation in der naturwissenschaftlichen Forschung: eine Studie aus sozialpsychologisch-semiotischer Perspektive
In: Journal für Psychologie ; 6 ; 2 ; 53-69 (2012)
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