Using Density Estimation to Improve Text Categorization

This paper explores the use of a statistical technique known as density estimation to potentially improve the results of text categorization systems which label documents by computing similarities between documents and categories. In addition to potentially improving a system's overall accuracy, density estimation converts similarity scores to probabilities. These probabilities provide confidence measures for a system's predictions which are easily interpretable and could potentially help to combine results of various systems. We discuss the results of three complete experiments on three separate data sets applying density estimation to the results of a TF*IDF/Rocchio system, and we compare these results to those of many competing approaches

STYLE AS EXEMPLIFICATIONAL ASPECT OF DISCOURSE

Goodmanova semiotika i teorija stila znatno utječu na suvremena promišljanja pojma stil i određivanja nadležnosti literarne stilistike, naročito u Francuskoj. Ključnim se u tome kontekstu pokazuje Goodmanov koncept egzemplifikacije kao specifične referencijalne funkcije. U ovome radu donosi se kritički pregled Goodmanovih teza i njegovih nastavljača, posebice G. Genettea.Goodman\u27s semiotics and the theory of style are highly influental in contemporary conteptualizations of style and in defining the scope of literary stylistics, particulary in France. The key concept in that context is exemplification – a specific referential function. This paper presents a critical summary of Nelson Goodman\u27s hypotheses as well as his successors\u27, namely Gérard Genette

NLP Found Helpful (at least for one Text Categorization Task)

Attempts to use natural language processing (NLP) for text categorization and information retrieval (IR) have had mixed results. Nevertheless, there is a strong intuition that NLP is important, at least for some tasks. In this paper, we discuss a task involving captioned images for which the subject and the predicate are critical. The usefulness of NLP for this task is established in two ways. In addition to the standard method of introducing a new system and comparing its performance with others in the literature, we also present evidence from experiments with human subjects showing that NLP generally improves speed and accuracy

Integration of Visual and Text-Based Approaches for the Content Labeling and Classification of Photographs

fsable, vh, bschiff

Integration of Visual and Text-Based Approaches for the Content Labeling and Classification of Photographs

Annotating photographs automatically with content descriptions facilitates organization, storage, and search over visual information. We present an integrated approach for scene classification that combines image-based and text-based approaches. On the text side, we use the text accompanying an image in a novel TF*IDF vector-based approach to classification. On the image side, we present a novel OF*IIF (object frequency) vector-based approach to classification. Objects are defined by clustering of segmented regions of training images. The image based OF*IIF approach is synergistic with the text based TF*IDF approach. By integrating the TF*IDF approach and the OF*IIF approach, we achieved a classification accuracy of 86%. This is an improvement of approximately 12% over existing image classifiers, an improvement of approximately 3% over the TF*IDF image classifier based on textual information, and an improvement of approximately 4% over the OF*IIF image classifier based on visual inform..

Contraception matters: indicators of poor usage of contraception in sexually active women attending family planning clinics in Victoria, Australia

<p>Abstract</p> <p>Background</p> <p>Unintended pregnancy (mistimed or unwanted) remains an important health issue for women. The purpose of this study was to determine the prevalence of and factors associated with risk of unintended pregnancy in a sample of Victorian women attending family planning clinics.</p> <p>Methods</p> <p>This cross-sectional survey of three Family Planning Victoria Clinics from April to July 2011 recruited <it>w</it>omen aged 16-50 years with a male sexual partner in the last 3 months, and not intending to conceive. The questionnaire asked about contraceptive behaviours and important factors that influence contraception use (identified from a systematic literature review). Univariate analysis was calculated for the variables of interest for associations with contraceptive use. An overall multivariate model for being at risk for unintended pregnancy (due to inconsistent or ineffective contraceptive use or non-use) was calculated through backward elimination with statistical significance set at <0.05.</p> <p>Results</p> <p>1006 surveys were analyzed with 96% of women reporting contraception use in the last 3 months. 37% of women were at risk for unintended pregnancy due to imperfect use (61% inconsistent users; 31% ineffective methods) or never using contraception (8%). On multivariate analysis, women at risk for unintended pregnancy compared with women not at risk were <25 years old (OR 1.8, 95% CI 1.2-2.7); had no university/postgraduate degree (OR 1.7, 95% CI 1.2-2.4); and had >1 partner in the last 3 months (OR 3.2, 95% CI 2.3-4.6). These women were dissatisfied with current contraception (OR 2.5, 95% 1.8-3.5); felt “vulnerable” to pregnancy (OR 2.1, 95% CI 1.6-3.0); were not confident in contraceptive knowledge (OR 2.6, 95% CI 1.5-4.8); were unable to stop to use contraception when aroused (OR 2.1, 95% CI 1.5-2.9) but were comfortable in speaking to a doctor about contraception (OR 2.3, 95% CI 1.1-4.1).</p> <p>Conclusion</p> <p>Despite reported high contraceptive usage, nearly 40% of women were at risk for unintended pregnancy primarily due to inconsistent contraceptive use and use of ineffective contraception. Strategies for improving consistency of effective contraception use or greater emphasis on long-acting contraception may be needed for certain subpopulations at higher risk for unintended pregnancy.</p