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A Corpus-based Approach to Finding Happiness
Date: March 2006
Creator: Liu, Hugo & Mihalcea, Rada, 1974-
Description: This paper discusses how to locate emotions. Abstract: What are the sources of happiness and sadness in everyday life? In this paper, the authors employ 'linguistic ethnography' to seek out where happiness lies in our everyday lives by considering a corpus of blogposts from the LiveJournal community annotated with happy and sad moods. By analyzing this corpus, the authors derive lists of happy and sad words and phrases annotated by their 'happiness factor'. Various semantic analyses performed with this wordlist reveal the happiness trajectory of a 24-day (3am and 9-10p are most happy), and a 7-day week (Wednesdays are saddest), and compare the socialness and human-centeredness of happy descriptions versus sad descriptions. The authors evaluate our corpus-based approach in a classification task and contrast our wordlist with emotionally-annotated wordlists produced by experimental focus groups. Having located happiness temporally and semantically within this corpus of everyday life, the paper concludes by offering a corpus-inspired livable recipe for happiness.
Contributing Partner: UNT College of Engineering
Permallink:digital.library.unt.edu/ark:/67531/metadc30980/
Cell Design to Maximize Capacity in CDMA Networks
Date: April 2002
Creator: Akl, Robert G.
Description: This presentation discusses the code division multiple access (CDMA) inter-cell effects, capacity regions, maximizing network capacity, mobility, a call admission control algorithm, and network performance.
Contributing Partner: UNT College of Engineering
Permallink:digital.library.unt.edu/ark:/67531/metadc30929/
CDMA Network Design
Date: May 2002
Creator: Akl, Robert G.
Description: This presentation gives an overview of code-division multiple access (CDMA) and inter-cell effects, network capacities, sensitivity analysis of base station locations, pilot-signal power, and transmission power of the mobiles, and concludes with numerical results.
Contributing Partner: UNT College of Engineering
Permallink:digital.library.unt.edu/ark:/67531/metadc30928/
Capacity Allocations in Multi-cell UMTS Networks for Different Spreading Factors with Perfect and Imperfect Power Control
Date: January 2006
Creator: Akl, Robert G. & Nguyen, Son
Description: This presentation discusses user and interference models, wideband code division multiple access (WCDMA) capacity with perfect and imperfect power control, and spreading factors with numerical results.
Contributing Partner: UNT College of Engineering
Permallink:digital.library.unt.edu/ark:/67531/metadc30937/
A Bootstrapping Method for Building Subjectivity Lexicons for Languages with Scarce Resources
Date: May 2008
Creator: Banea, Carmen; Wiebe, Janyce M. & Mihalcea, Rada, 1974-
Description: Abstract: This paper introduces a method for creating a subjectivity lexicon for languages with scarce resources. The method is able to build a subjectivity lexicon by using a small seed set of subjectivity words, and online dictionary, and a small raw corpus, coupled with a bootstrapping process that ranks new candidate words based on a similarity measure. Experiments performed with a rule-based sentence level subjectivity classifier show an 18% absolute improvement in F-measure as compared to previously proposed semi-supervised methods.
Contributing Partner: UNT College of Engineering
Permallink:digital.library.unt.edu/ark:/67531/metadc31002/
Building Multilingual Semantic Networks with Non-Expert Contributions over the Web
Date: November 2003
Creator: Ayewah, Nathanial; Mihalcea, Rada, 1974- & Nastase, Vivi
Description: This paper discusses building multilingual semantic networks. Abstract: We present a system that allows non-expert Web users to contribute towards building a multilingual lexical resource. Our study focuses on the Romanian-English language pair, and the target resource is a Romanian WordNet strongly connected to the English WordNet. We use a bilingual dictionary, a monolingual definition dictionary and documents on the Web to build synsets, attach them a gloss, and provide some examples. The results of the semi-automatic acquisition system are judged by two human judges, and they are compared to automatic approaches to building a Romanian WordNet.
Contributing Partner: UNT College of Engineering
Permallink:digital.library.unt.edu/ark:/67531/metadc30947/
PicNet: Augmenting Semantic Resources with Pictorial Representations
Date: March 2005
Creator: Borman, Andy; Mihalcea, Rada & Tarau, Paul
Description: In this paper, the authors introduce PicNet, a Web-based system for augmenting semantic resources with illustrative images using volunteer contributions over the Web. PicNet seeks to build rich knowledge-bases that encode word/image associations, to the end of combining the advantages and power of both visual and linguistic representations as means of defining world concepts. In this paper, the authors address some of the issues encountered in identifying prototypical illustrations for various concepts, as well as issues related to the construction of such pictorial knowledge-bases with the help of Web users.
Contributing Partner: UNT College of Engineering
Permallink:digital.library.unt.edu/ark:/67531/metadc30972/
Amazon Mechanical Turk for Subjectivity Word Sense Disambiguation
Date: June 2010
Creator: Akkaya, Cem; Conrad, Alexander; Wiebe, Janyce & Mihalcea, Rada
Description: This paper discusses word sense disambiguation. Abstract: Amazon Mechanical Turk (MTurk) is a marketplace for so-called "human intelligence tasks" (HITs), or tasks that are easy for humans but currently difficult for automated processes. Providers upload tasks to MTurk which workers then complete. Natural language annotation is one such human intelligence task. In this paper, the authors investigate using MTurk to collect annotations for Subjectivity Word Sense Disambiguation (SWSD), a course-grained word sense disambiguation task. The authors investigate whether they can use MTurk to acquire good annotations with respect to gold-standard data, whether they can filter out low-quality workers (spammers), and whether there is a learning effect associated with repeatedly completing the same kind of task. While our results with respect to spammers are inconclusive, the authors are able to obtain high-quality annotations for the SWSD task. These results suggest a greater role for MTurk with respect to constructing a large scale SWSD system in the future, promising substantial improvement in subjectivity and sentiment analysis.
Contributing Partner: UNT College of Engineering
Permallink:digital.library.unt.edu/ark:/67531/metadc31023/
An Algorithm for Open Text Semantic Parsing
Date: August 2004
Creator: Shi, Lei & Mihalcea, Rada
Description: Abstract: This paper describes an algorithm for open text shallow semantic parsing. The algorithm relies on a frame dataset (FrameNet) and a semantic network (WordNet), to identify semantic relations between words in open text, as well as shallow semantic features associated with concepts in the text. Parsing semantic structures allows semantic units and constituents to be accessed and processed in a more meaningful way than syntactic parsing, moving the automation of understanding natural language text to a higher level.
Contributing Partner: UNT College of Engineering
Permallink:digital.library.unt.edu/ark:/67531/metadc30953/
Classifier Stacking and Voting for Text Filtering
Date: November 2002
Creator: Mihalcea, Rada, 1974-
Description: Abstract: This paper summarizes the approach and the results of the TextCat system participating in the Filtering track in the Text Retrieval Conference 2002. The system relies primarily on statistical methods, and was designed with the main purpose of having a backbone system in which we can further integrate semantic components, and evaluate their relative performance as compared to traditional statistical approaches. They system is therefore simple, and is based on techniques for keywords extraction, and various classifier combinations including stacking and voting. TextCat participated in the Batch and Routing tasks. In the Batch task, it achieved a score of 39.02% normalized utility, and 26.37% F-measure respectively, averaged over all topics. The averaged uninterpolated precision for our best routing submission was 14.16%.
Contributing Partner: UNT College of Engineering
Permallink:digital.library.unt.edu/ark:/67531/metadc30942/