Text documents are complex high dimensional objects. To effectively visualize such data it is important to reduce its dimensionality and visualize the low dimensional embedding as a 2-D or 3-D scatter plot. In this paper we explore dimensionality reduction methods that draw upon domain knowledge in order to achieve a better low dimensional embedding and visualization of documents. We consider the use of geometries specified manually by an expert, geometries derived automatically from corpus statistics, and geometries computed from linguistic resources.
We focus in this paper on visualizing a corpus of text documents using a 2-D scatter plot. While this is perhaps the most popular and practical text visualization technique, other methods such as @cite_18 , @cite_9 , @cite_20 , @cite_5 , @cite_4 , @cite_11 exist. It is conceivable that the techniques developed in this paper may be ported to enhance these alternative visualization methods as well.
@cite_18 @cite_4 @cite_9 @cite_5 @cite_20 @cite_11
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This paper introduces a novel representation, called the InfoCrystal, that can be used as a visualization tool as well as a visual query language to help users search for information. The InfoCrystal visualizes all the possible relationships among N concepts. Users can assign relevance weights to the concepts and use thresholding to select relationships of interest. The InfoCrystal allows users to specify Boolean as well as vector-space queries graphically. Arbitrarily complex queries can be created by using the InfoCrystals as building blocks and organizing them in a hierarchical structure. The InfoCrystal enables users to explore and filter information in a flexible, dynamic and interactive way. > 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. TextTiling is a technique for subdividing texts into multi-paragraph units that represent passages, or subtopics. The discourse cues for identifying major subtopic shifts are patterns of lexical co-occurrence and distribution. The algorithm is fully implemented and is shown to produce segmentation that corresponds well to human judgments of the subtopic boundaries of 12 texts. Multi-paragraph subtopic segmentation should be useful for many text analysis tasks, including information retrieval and summarization. TextArc is an alternate view of a text, tailored to expose the frequency and distribution of the words of an entire text on a single page or screen. In texts having no markup or metainformation, one of the quickest ways of getting a feeling for the content of a text is to scan through the words that are used most frequently. Knowing the distribution of those words in the text can support another level of understanding, e.g. helping to reveal chapters in a text that concentrate on a specific subject. A structure and method of displaying an entire text on a single page or screen is presented. It reveals both frequency and distribution, and provides a well-understood and organized space that works as a background for other tools. The ThemeRiver visualization depicts thematic variations over time within a large collection of documents. The thematic changes are shown in the context of a time-line and corresponding external events. The focus on temporal thematic change within a context framework allows a user to discern patterns that suggest relationships or trends. For example, the sudden change of thematic strength following an external event may indicate a causal relationship. Such patterns are not readily accessible in other visualizations of the data. We use a river metaphor to convey several key notions. The document collection's time-line, selected thematic content and thematic strength are indicated by the river's directed flow, composition and changing width, respectively. The directed flow from left to right is interpreted as movement through time and the horizontal distance between two points on the river defines a time interval. At any point in time, the vertical distance, or width, of the river indicates the collective strength of the selected themes. Colored "currents" flowing within the river represent individual themes. A current's vertical width narrows or broadens to indicate decreases or increases in the strength of the individual theme. Documents and other categorical valued time series are often characterized by the frequencies of short range sequential patterns such as n-grams. This representation converts sequential data of varying lengths to high dimensional histogram vectors which are easily modeled by standard statistical models. Unfortunately, the histogram representation ignores most of the medium and long range sequential dependencies making it unsuitable for visualizing sequential data. We present a novel framework for sequential visualization of discrete categorical time series based on the idea of local statistical modeling. The framework embeds categorical time series as smooth curves in the multinomial simplex summarizing the progression of sequential trends. We discuss several visualization techniques based on the above framework and demonstrate their usefulness for document visualization.