Variation of word frequencies across genre classification tasks

This paper examines automated genre classification of text documents and its role in enabling the effective management of digital documents by digital libraries and other repositories. Genre classification, which narrows down the possible structure of a document, is a valuable step in realising the general automatic extraction of semantic metadata essential to the efficient management and use of digital objects. In the present report, we present an analysis of word frequencies in different genre classes in an effort to understand the distinction between independent classification tasks. In particular, we examine automated experiments on thirty-one genre classes to determine the relationship between the word frequency metrics and the degree of its significance in carrying out classification in varying environments

Feature Type Analysis in Automated Genre Classification

In this paper, we compare classifiers based on language model, image, and stylistic features for automated genre classification. The majority of previous studies in genre classification have created models based on an amalgamated representation of a document using a multitude of features. In these models, the inseparable roles of different features make it difficult to determine a means of improving the classifier when it exhibits poor performance in detecting selected genres. By independently modeling and comparing classifiers based on features belonging to three types, describing visual, stylistic, and topical properties, we demonstrate that different genres have distinctive feature strengths.

The Open Research Web: A Preview of the Optimal and the Inevitable

The multiple online research impact metrics we are developing will allow the rich new database , the Research Web, to be navigated, analyzed, mined and evaluated in powerful new ways that were not even conceivable in the paper era – nor even in the online era, until the database and the tools became openly accessible for online use by all: by researchers, research institutions, research funders, teachers, students, and even by the general public that funds the research and for whose benefit it is being conducted: Which research is being used most? By whom? Which research is growing most quickly? In what direction? under whose influence? Which research is showing immediate short-term usefulness, which shows delayed, longer term usefulness, and which has sustained long-lasting impact? Which research and researchers are the most authoritative? Whose research is most using this authoritative research, and whose research is the authoritative research using? Which are the best pointers (“hubs”) to the authoritative research? Is there any way to predict what research will have later citation impact (based on its earlier download impact), so junior researchers can be given resources before their work has had a chance to make itself felt through citations? Can research trends and directions be predicted from the online database? Can text content be used to find and compare related research, for influence, overlap, direction? Can a layman, unfamiliar with the specialized content of a field, be guided to the most relevant and important work? These are just a sample of the new online-age questions that the Open Research Web will begin to answer

Retrieval Models for Genre Classification

Genre provides a characterization of a document with respect to its form or functional trait. Genre is orthogonal to topic, rendering genre information a powerful filter technology for information seekers in digital libraries. However, an efficient means for genre classification is an open and controversially discussed issue. This paper gives an overview and presents new results related to automatic genre classification of text documents. We present a comprehensive survey which contrasts the genre retrieval models that have been developed for Web and non-Web corpora. With the concept of genre-specific core vocabularies the paper provides an original contribution related to computational aspects and classification performance of genre retrieval models: we show how such vocabularies are acquired automatically and introduce new concentration measures that quantify the vocabulary distribution in a sensible way. Based on these findings we construct lightweight genre retrieval models and evaluate their discriminative power and computational efficiency. The presented concepts go beyond the existing utilization of vocabulary-centered, genre-revealing features and open new possibilities for the construction of genre classifiers that operate in real-time

Building a document genre corpus: a profile of the KRYS I corpus

This paper describes the KRYS I corpus, consisting of documents classified into 70 genre classes. It has been constructed as part of an effort to automate document genre classification as distinct from topic detection. Previously there has been very little work on building corpora of texts which have been classified using a nontopical genre palette. The reason for this is partly due to the fact that genre as a concept, is rooted in philosophy, rhetoric and literature, and highly complex and domain dependent in its interpretation ([11]). The usefulness of genre in everyday information search is only now starting to be recognised and there is no genre classification schema that has been consolidated to have applicable value in this direction. By presenting here our experiences in constructing the KRYS I corpus, we hope to shed light on the information gathering and seeking behaviour and the role of genre in these activities, as well as a way forward for creating a better corpus for testing automated genre classification tasks and the application of these tasks to other domains.

Making the FTC ☺: An Approach to Material Connections Disclosures in the Emoji Age

In examining the rise of influencer marketing and emoji’s concurrent surge in popularity, it naturally follows that emoji should be incorporated into the FTC’s required disclosures for sponsored posts across social media platforms. While current disclosure methods the FTC recommends are easily jumbled or lost in other text, using emoji to disclose material connections would streamline disclosure requirements, leveraging an already-popular method of communication to better reach consumers. This Note proposes that the FTC adopts an emoji as a preferred method of disclosure for influencer marketing on social media. Part I discusses the rise of influencer marketing, the FTC and its history of regulating sponsored content, and the current state of regulation. Part II explores the proliferation of emoji as a method of communication, and the role of the Unicode Consortium in regulating the adoption of new emoji. Part III makes the case for incorporating emoji as a method of disclosure to bridge compliance gaps, and offers additional recommendations to increase compliance with existing regulations

Detecting Family Resemblance: Automated Genre Classification.

This paper presents results in automated genre classification of digital documents in PDF format. It describes genre classification as an important ingredient in contextualising scientific data and in retrieving targetted material for improving research. The current paper compares the role of visual layout, stylistic features and language model features in clustering documents and presents results in retrieving five selected genres (Scientific Article, Thesis, Periodicals, Business Report, and Form) from a pool of materials populated with documents of the nineteen most popular genres found in our experimental data set.

Building a Document Genre Corpus: a Profile of the KRYS I Corpus

This paper describes the KRYS I corpus (http://www.krys-corpus.eu/Info.html), consisting of documents classified into 70 genre classes. It has been constructed as part of an effort to automate document genre classification as distinct from topic detection. Previously there has been very little work on building corpora of texts which have been classified using a non-topical genre palette. The reason for this is partly due to the fact that genre as a concept, is rooted in philosophy, rhetoric and literature, and highly complex and domain dependent in its interpretation ([11]). The usefulness of genre in everyday information search is only now starting to be recognised and there is no genre classification schema that has been consolidated to have applicable value in this direction. By presenting here our experiences in constructing the KRYS I corpus, we hope to shed light on the information gathering and seeking behaviour and the role of genre in these activities, as well as a way forward for creating a better corpus for testing automated genre classification tasks and the application of these tasks to other domains

English Literacy Development for English Language Learners: Does Spanish Instruction Promote or Hinder?

In this brief, the authors consider whether instruction in a child\u27s native language (particularly Spanish) hinders or promotes learning of literacy in English. The authors conduct a four-step process for identifying research on this topic, examining this literature, and then determining the answer to this clinical question. The results suggest that supporting a child\u27s home/native language promotes rather than hinders development of English literacy skills