Insights into social phenomenon can be gleaned from trends and patterns in corpora of documents associated with that phenomenon. Recent years have witnessed the use of computational techniques, mostly based on keywords, to analyze large corpora for these purposes. In this paper, we extend these techniques to incorporate semantic features. We introduce Doris, an interactive exploration tool that combines semantic features with information retrieval techniques to enable exploration of document corpora corresponding to the social phenomenon. We discuss the semantic techniques and describe an implementation on a corpus of United States (US) presidential speeches. We illustrate, with examples, how the ability to combine syntactic and semantic features in a visualization helps researchers more easily gain insights into the underlying phenomenon.
In addition to the work involving the use of computational techniques in the social sciences, we draw on work on Topic Models @cite_2 and word embeddings @cite_12 . Our interface is influenced by the work of Freeman and Gelernter @cite_11 , in which they introduced the idea of temporal presentation of a set of documents. Bergman, Beyth-Marom, et. al. @cite_0 adapted this work to the context of search interfaces. Recent years have seen the adoption of this class of interfaces in widely used software systems, including the Apple Mac interface.
@cite_0 @cite_11 @cite_12 @cite_2
2003482363 2047997333 2141599568 1880262756
Traditionally users access their personal files mainly by usingfolder navigation. We evaluate whether recent improvements indesktop search have changed this fundamental aspect of PersonalInformation Management (PIM). We tested this in two studies usingthe same questionnaire: (a) The Windows Studya longitudinalcomparison of Google Desktop and Windows XP SearchCompanion, and (b) The Mac Studya large scale comparison of MacSpotlight and Sherlock. There were few effects forimproved search. First, regardless of search engine, there was astrong navigation preference: on average, users estimated that theyused navigation for 56-68 of file retrieval events but searchedfor only 4-15 of events. Second, the effect of improving thequality of the search engine on search usage was limited andinconsistent. Third, search was used mainly as a last resort whenusers could not remember file location. Finally, there was noevidence that using improved desktop search engines leads people tochange their filing habits to become less reliant on hierarchicalfile organization. We conclude by offering theoretical explanationsfor navigation preference, relating to differences between PIM andInternet retrieval, and suggest alternative design directions forPIM systems. Conventional software systems, such as those based on the “desktop metaphor,” are ill-equipped to manage the electronic information and events of the typical computer user. We introduce a new metaphor, Lifestreams, for dynamically organizing a user's personal workspace. Lifestreams uses a simple organizational metaphor, a time-ordered stream of documents, as an underlying storage system. Stream filters are used to organize, monitor and summarize information for the user. Combined, they provide a system that subsumes many separate desktop applications. This paper describes the Lifestreams model and our prototype system. Continuous space language models have recently demonstrated outstanding results across a variety of tasks. In this paper, we examine the vector-space word representations that are implicitly learned by the input-layer weights. We find that these representations are surprisingly good at capturing syntactic and semantic regularities in language, and that each relationship is characterized by a relation-specific vector offset. This allows vector-oriented reasoning based on the offsets between words. For example, the male female relationship is automatically learned, and with the induced vector representations, “King Man + Woman” results in a vector very close to “Queen.” We demonstrate that the word vectors capture syntactic regularities by means of syntactic analogy questions (provided with this paper), and are able to correctly answer almost 40 of the questions. We demonstrate that the word vectors capture semantic regularities by using the vector offset method to answer SemEval-2012 Task 2 questions. Remarkably, this method outperforms the best previous systems. We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.